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This volume is another endeavour undertaken by the IEEE Computational Intelligence Society (CIS)'s Task Force on Security, Surveillance and Defense (SSD) and a step in the right direction of consolidating and disseminating the role of Computational Intelligence (CI) techniques in the design, development and deployment of security and defense solutions. The book serves as an excellent guide for surveying the state of the art in CI employed within SSD projects or programs. The reader will find in its pages how CI has contributed to solve a wide range of challenging problems, ranging from the detection of buried explosive hazards in a battlefield to the control of unmanned underwater vehicles, the delivery of superior video analytics for protecting critical infrastructures or the development of stronger intrusion detection systems and the design of military surveillance networks, just to name a few. Defense scientists, industry experts, academicians and practitioners alike (mostly in computer science, computer engineering, applied mathematics or management information systems) will all benefit from the wide spectrum of successful application domains compiled in this volume. Senior undergraduate or graduate students may also discover in this volume uncharted territory for their own research endeavors.
Recent Advances in Computational
Intelligence in Defense and Security
Rami Abielmona, Rafael Falcon, Nur Zincir-Heywood
and Hussein Abbass
1 Introduction
Given the rapidly changing and increasingly complex nature of global security,we
continue to witness a remarkable interest within the defense and security commu-
nities in novel, adaptive and resilient techniques that can cope with the challenging
problems arising in this domain. These challenges are brought forth not only by the
overwhelming amount of data reported by a plethora of sensing and tracking modal-
ities, but also by the emergence of innovative classes of decentralized, mass-scale
communication protocols and connectivity frameworks such as cloud computing
[5], sensor and actuator networks [7], intelligent transportation systems [1], wear-
able computing [2] and the Internet of Things [6]. Realizing that traditional tech-
niques have left many important problems unsolved, and in some cases, not ade-
quately addressed, further efforts have to be undertaken in the quest for algorithms
and methodologies that can accurately detect and easily adapt to emerging threats.
Computational Intelligence (CI) [4] lies at the forefront of many algorithmic
breakthroughs that we are witnessing nowadays. This vibrant research discipline
offers a broad set of tools that can deal with the imprecision and uncertainty prevalent
in the real world and can effectively tackle ill-posed problems for which traditional
(i.e., hard computing) schemes do not provide either a feasible or an efficient solu-
tion. The term CI is not exclusive to a single methodology; rather, it acts as a large
umbrella under which several biologically and linguistically motivated techniques
have been developed [3]—some of them enjoying unprecedented popularity these
days [4]. CI has expanded its traditional foundation (pillared on artificial neural net-
works,fuzzy systems and evolutionary computation) to accommodate other related
R. Abielmona R. Falcon ()
Larus Technologies Corporation, 170 Laurier Ave West - Suite 310,
Ottawa, ON K1P 5V5, Canada
N. Zincir-Heywood
Dalhousie University, Halifax, Canada
H. Abbass
University of New South Wales, Canberra, Australia
© Springer International Publishing Switzerland 2016
R. Abielmona et al. (eds.), Recent Advances in Computational Intelligence
in Defense and Security, Studies in Computational Intelligence 621,
DOI 10.1007/978-3-319-26450-9_1
2 R. Abielmona et al.
problem-solving approaches that have recently emerged and also functionally pursue
the same goals of tractability, robustness and low solution cost [3,4], including but
not withstanding: rough sets,multi-valued logic,connectionist systems,swarm intel-
ligence,artificial immune systems,granular computing,game theory,deep learning
and the hybridization of the aforementioned systems.
As a recognition of the influence CI algorithms are increasingly having upon the
security and defense realm, the IEEE Computational Intelligence Society (CIS) cre-
ated a Task Force on Security, Surveillance and Defense1(SSD) in February 2010
to showcase recent and ongoing efforts in the application of CI methods to the SSD
domain. The flagship event organized by the Task Force, as a forum to exchange ideas
and contributions in these topics, is the IEEE Symposium on Computational Intel-
ligence for Security and Defense Applications (CISDA), which originated in 2007
and has been annually held since 2009. Other related initiatives are the Computa-
tional Intelligence for Security, Surveillance and Defense (CISSD) Special Session
held at WCCI 2010/2014 and at SSCI 2011/2013; the Soft Computing applied to
Security and Defense (SoCoSaD) Special Session organized under ECTA 2014; the
Workshop on Genetic and Evolutionary Computation in Defense, Security and Risk
Management held during GECCO 2014 and 2015; and the Canadian Tracking and
Fusion Group (CTFG) annual workshops since 2011.
This volume is another endeavour undertaken by the IEEE CIS SSD Task Force
and a step in the right direction of consolidating and disseminating the role of CI
techniques in the design, development and deployment of security and defense solu-
tions. The book serves as an excellent guide for surveying the state of the art in CI
employed within SSD projects or programs. The reader will find in its pages how
CI has contributed to solve a wide range of challenging problems, ranging from the
detection of buried explosive hazards in a battlefield to the control of unmanned
underwater vehicles, the delivery of superior video analytics for protecting criti-
cal infrastructures or the development of stronger intrusion detection systems and
the design of military surveillance networks, just to name a few. Defense scientists,
industry experts, academicians and practitioners alike (mostly in computer science,
computer engineering, applied mathematics or management information systems)
will all benefit from the wide spectrum of successful application domains compiled
in this volume. Senior undergraduate or graduate students may also discover in this
volume uncharted territory for their own research endeavors.
We received 53 initial submissions in November 2014 as a response to the Call
for Book Chapters, out of which 25 were accepted following the recommendations
emanating from the peer-review process conducted by the Technical Program Com-
mittee composed of 74 experts and researchers in the field from 22 countries. The 25
accepted chapters were co-authored by 75 contributors from the following countries:
Australia (2), Belgium (1), Canada (24), China (1), Cuba (3), India (5), Italy (9),
Saudi Arabia (1), Singapore (3), Spain (7), Thailand (3), Tunisia (1), UK (2) and
USA (13). It is important to note that 73 % of the contributors are affiliated with
academic institutions, 17 % with industry and the remaining 10 % with government.
Recent Advances in Computational Intelligence in Defense and Security 3
1.1 Volume Organization
The book is structured into five major parts corresponding to the themes that natu-
rally emerged out of the accepted contributions, i.e., physical, cyber and biometric
security, situational/threat assessment and mission planning/resource optimization.
They are representative of five strategic areas within defense and security that evi-
dence the burgeoning interest of the CI community in developing cutting-edge solu-
tions to entangled problems therein.
Part I: Physical Security and Surveillance [4 chapters]
The problem of detecting buried explosive hazards using forward-looking infrared
and ground-penetrating radar sensors is described in Chap. 2Computational intel-
ligence methods in forward-looking explosive hazard detection”. The authors elabo-
rate on the prescreening phase (detection of candidate points in the image) and then
on the classification phase. They report the performance of different approaches in
the latter phase, ranging from kernel methods to more advanced algorithms like deep
belief and convolutional networks to learn new image space features and descriptors.
In the Chap. 3entitled “Classification-driven video analytics for critical
infrastructure protection”, the authors are concerned with alleviating the burden of
an operator that constantly monitors several video feeds to detect suspicious activi-
ties around a secured critical infrastructure. The automated solution proposed in this
chapter extracts the objects of interest (i.e., car, person, bird, ship) from the image
using an iteratively updated background subtraction method, then the object is classi-
fied by an artificial neural network (ANN) coupled to a temporal Bayesian filter. The
next step is determining the behavior of the object, e.g., entering a restricted zone
or stopping and dropping an object. Relevant alerts are issued to the operator should
a suspicious event be identified. The authors tried their approach in the automated
monitoring of a dumpster, a doorway and a port.
A model-based event correlation framework for critical infrastructure surveil-
lance is put forward in Chap. 4Fuzzy decision fusion and multiformalism modeling
in physical security monitoring”. The framework named DETECT (DEcision Trig-
gering Event Composer & Tracker) stores detected threat scenarios using event trees
and then recognizes those scenarios in real time. A multiformalism approach for
the evaluation of fuzzy detection probabilities using fuzzy operators upon Bayesian
Networks and Generalized Stochastic Petri Nets is presented. The authors consid-
ered a threat scenario of a terrorist attack in a metro railway station to illustrate the
applicability of their methodology.
Chapter 5Intelligent radar signal recognition and classification” investigates a
classification problem for timely and reliable identification of radar signal emitters
by implementing and following an ANN-based approach. The idea is to determine
the type of radar given certain characteristics of its signal described by a group of
attributes (some of them having missing values). Two separate approaches were con-
sidered. In the first one, missing values are removed using listwise deletion and then
a feedforward neural network is used for classification. The other approach leans
on a multiple-imputation method to produce unbiased estimates of the missing data
4 R. Abielmona et al.
before it is passed to the ANN. In both cases, competitive classification accuracies
were obtained.
Part II: Cyber Security and Intrusion Detection Systems [5 chapters]
Chapter 6An improved decision system for URL accesses based on a rough fea-
ture selection technique” addresses corporate security; in particular, internal security
breaches caused by employees accessing dangerous Internet locations. The authors
propose a classification system that detects anomalous and potentially insecure sit-
uations by learning from existing white (allowed) and black (forbidden) URL lists.
It then decides whether an unseen new URL should be allowed or denied. The sys-
tem’s performance is boosted by the removal of irrelevant features (guided by rough
set theory) and handling class imbalances, with a reported classification accuracy
reaching about 97 %.
Chapter 7A granular intrusion detection system using rough cognitive net-
works”, the authors designed an intrusion detection system from a Granular Com-
puting angle to classify network traffic as either normal or abnormal. The proposed
methodology relies on rough cognitive networks (RCNs), a recently introduced gran-
ular system that combines the causal representation inherent to fuzzy cognitive maps
with the imprecision-handling abilities provided by rough set theory. The RCN para-
meters are learned from data using Harmony Search as the underlying optimization
engine. RCNs were evaluated against seven other traditional classifiers and were
found to be a competitive model that produces high detection rates and low false
alarm rates.
Chapter 8NNCS: randomization and informed search for novel naval cyber
strategies” argues that software security can be improved by providing adequate
degrees of redundancy and diversity to counter both hardware and software faults.
The proposed scheme relies on component rule bases written in a schema-based Very
High Level Language. Deviations from the constructed model are likely indicators
of a cyber attack. The authors illustrate the benefits of their proposal with a battle
management example.
Developing classifiers that can identify sophisticated types of cyber attacks is
the main goal of Chap. 9Semi-supervised classification system for the detection of
Advanced Persistent Threats”. The authors define an anomaly score metric to detect
the most anomalous subsets of traffic data. The human expert is then required to label
the instances within this set, after which a classifier is built based on both labeled and
unlabeled data. Genetic programming, decision trees and support vector machines
were independently used to construct the classifier.
Chapter 10 A benchmarking study on stream network traffic analysis using active
learning” aims at comparing the performance of previously existing active learning
and query budgeting strategies as well as an adaptive ANN approach on streaming
network traffic to detect malicious network activity such as botnets. The analysis
revolves around two new metrics that account for class imbalance as well as the
traditional accuracy and detection rate measures. Results are quite encouraging and
confirm that the Hoeffding Tree classifier behaves particularly well on the data sets
under consideration.
Recent Advances in Computational Intelligence in Defense and Security 5
Part III: Biometric Security and Authentication Systems [5 chapters]
Handwritten signatures have long been used as an authentication system given that
they are intrinsically endowed with specificity related to an individual. In Chap. 11
Visualization of handwritten signatures based on haptic information”, the authors
discuss how to integrate haptic technologies to capture other aspects like kinesthetic
and tactile feedback from the user. The study is centered around visualizing and
understanding the internal structure of the haptic data (position, force, torque and
orientation) in an unsupervised fashion. Special emphasis is made on several dimen-
sionality reduction methods, including CI-based ISOMAP and Genetic Program-
Reducing the number of false positives in a biometric identification system is at
the heart of Chap. 12 Extended metacognitive neuro-fuzzy inference system for bio-
metric identification”. The authors introduce a neurofuzzy inference system along
with a sequential evolving learning algorithm as a cognitive component of an archi-
tecture that also features a metacognitive component. The latter is responsible for
actively regulating the learning of the cognitive component by deciding what, when
and how to learn from the available data. The proposed architecture is first bench-
marked on a set of medical datasets and then on two real-world biometric security
applications, namely signature verification and fingerprint recognition. The compar-
ison with four other authentication systems confirms that the proposed architecture
yields a superior performance.
Travel documentation at this time relies either on paper documents or on elec-
tronic systems requiring connectivity to core servers and databases for verification
purposes. Chapter 13 Privacy, security and convenience: biometric encryption for
smartphone-based electronic travel documents” proposes a new paradigm for issu-
ing, storing and verifying travel documents. This smartphone-based approach enables
a new kind of biometric checkpoint to be placed at key points throughout the interna-
tional voyage that does not require storage of biometric information, which simplifies
things from a policy and privacy perspective. The authors expect their architecture
to enhance system security as well as the privacy and convenience of international
Digital watermarking allows enforcing authenticity and integrity of an image,
which is a major security concern for many industries. The optimization of the
embedding parameters for a bi-tonal watermarking system is pursued in Chap.14 A
dual-purpose memory approach for dynamic particle swarm optimization of recur-
rent problems”. The authors propose a memory-based Dynamic Particle Swarm
Optimization method. This memory can operate in either generative or regression
mode and is implemented via a Gaussian Mixture Model of candidate solutions
estimated in the optimization space, which provides a compact representation of
previously found PSO solutions. Results indicate that the computational burden of
this watermarking problem is reduced by up to 90.4 % with negligible impact on
Chapter 15 Risk assessment in authentication machines” presents an approach
for building a risk profiler for use in authentication machines. The proposed risk
profiler provides a risk assessment at all phases of the authentication machine
6 R. Abielmona et al.
life-cycle. The key idea is to utilize the advantages of belief networks to solve large-
scale multi-source fusion problems. The authors have extended the abilities of belief
networks by incorporating Dempster-Shafer Theory measures. The main goal is to
increase the reliability of security risk assessment for authentication machines using
the computational-intelligence-based fusion of results from different models, met-
rics, and philosophies of decision-making under uncertainty.
Part IV: Situational Awareness and Threat Assessment [5 chapters]
To counter piracy attempts, maritime operators need to quickly and effectively
allocate some mobile resources (defender units) to assist a target given the avail-
able information about the attackers. In Chap. 16 Game theoretical approach for
dynamic active patrolling in a counter-piracy framework”, the authors introduce a
decision support system (DSS) to that end. The DSS has been designed using Game
Theory in order to handle the attractiveness of targets and model strategies for attack-
ers and defenders. Game Theory has proved to be a robust tool to identify the best
strategy for the defenders given the information and capabilities of opponents. In
the proposed framework, the optimal strategy is modeled as the equilibrium of a
time-varying Bayesian-Stackelberg game.
A naval mine is an underwater explosive device meant to damage or destroy sur-
face ships or submarines. An influence mine is a type of naval mine that is trig-
gered by the influence of a vessel or submarine rather than requiring direct contact
with it. The ship classification unit (SCU) of an influence mine determines whether
the sensed vessel is a target or not, which will cause it to detonate accordingly. In
Chap. 17 mspMEA: the microcones separation parallel multiobjective evolutionary
algorithm and its application to fuzzy rule-based ship classification”, the author uses
a parallel multiobjective evolutionary algorithm (MOEA) based on the concept of
microcones to speed up the optimization of the fuzzy rule-based classifiers used to
emulate the SCU contained in modern influence mines. A speedup factor of 16.58 %
was achieved over a cone-based MOEA algorithm.
Detecting a target in a Synthetic Aperture Radar (SAR) image is a challenging
issue since SAR images do not look similar to optical images at all. In Chap.18
Synthetic aperture radar (SAR) automatic target recognition (ATR) using fuzzy
co-occurrence matrix texture features”, the authors put forward a methodology for
detecting three types of military vehicles from SAR images without using any pre-
processing methods. The texture features generated from the fuzzy co-occurrence
matrix are passed on to a multi-class SVM and to a radial basis function (RBF) neural
network. The ensemble average is utilized as an information fusion tool. The classi-
fication results are superior to those obtained via gray level co-occurrence matrices.
Text mining techniques are important for security and defense applications since
they allow detecting possible threats to security and public safety (such as mentions
of terrorist activities or extremist/radical texts). Chapter 19 Text mining in social
media for security threats” discusses information extraction techniques from social
media texts (Twitter in particular) and showcases two applications that make use of
these techniques: (1) extracting the locations mentioned in tweets and (2) inferring
the users’ location based on all the tweets generated by each user. The former task
Recent Advances in Computational Intelligence in Defense and Security 7
is accomplished via a sequence-based classifier followed by disambiguation rules
whereas the latter is tackled through deep neural networks.
The increasing worldwide use of mobile devices has also sparked a growing num-
ber of malware apps that should be automatically flagged and vetted by security
researchers. Chapter 20 DroidAnalyst: synergic Android framework for static and
dynamic app analysis” features an automated web-based app vetting and malware
analysis framework for Android devices that integrates the synergy of static and
dynamic analysis to improve the accuracy and efficiency of the identification process.
DroidAnalyst generates a unified analysis model that combines the strengths of the
complementary approaches with multiple detection methods to boost the app code
analysis. Machine learning methods such as random forests are employed to gener-
ate a set of features with multiple detection methods based on the static and dynamic
module analysis.
Part V: Strategic/Mission Planning and Resource Management
[6 chapters]
Chapter 21 Design and development of intelligent military training systems and
wargames” elaborates on an architectural approach for designing composable, multi-
service and joint wargames that can meet the requirements of several military estab-
lishments. This architecture is realized by the design and development of common
components that are reused across applications and variable components that are
customizable to different training establishments’ training simulators. Some of the
important CI techniques (such as fuzzy cognitive maps, game trees, case-based rea-
soning, genetic algorithms and fuzzy rule-based systems) that are used to design
these wargame components are explained with suitable examples, followed by their
applications to two specific cases of Joint Warfare Simulation System and an Inte-
grated Air Defence Simulation System for air-land battles.
Due to operational requirements, helicopters are now being frequently used for
missions beyond what their original design permits. There is thus the need to moni-
tor their usage and more accurately determine the life of its critical components. The
methodology outlined in Chap. 22 Improving load signal and fatigue life estimation
for helicopter components using computational intelligence techniques” enables the
prediction of the load signals (i.e., the time-varying measurement of the load) on the
helicopter components using existing flight data and avoiding the installation of addi-
tional sensors. The prediction is performed by means of CI techniques (e.g., fuzzy
sets, neural networks, evolutionary algorithms) and statistical techniques (e.g., resid-
ual variance analysis). The predicted load signals then form the basis for estimating
the fatigue life of the component, i.e., the length of time that the component can be
safely operated with minimal or acceptable risk of failure. The presented techniques
certainly attained a more accurate prediction of the peak values in the load signal.
Defense and security organizations rely on the use of scenarios for a wide range of
activities. Scenarios normally take the form of linguistic stories, whereby a picture of
a context is painted using storytelling principles. In Chap. 23 Evolving narrations of
strategic defense and security scenarios for computational scenario planning”, the
authors illustrate how evolutionary computation techniques can be used to evolve
8 R. Abielmona et al.
different narrations of a strategic story. A representation of a story is put forth that
allows evolution to operate on it in a simple manner. Through a set of linguistic
constraints and transformations, it is guaranteed that any random chromosome gets
transformed into a unique coherent and causally consistent story. The same repre-
sentation could be used to design simulation models that evaluate these stories. The
proposed approach paves the way for automating the evaluation process of defense
and security scenarios on multiple levels of resolution, starting from a grand strategic
level down to a tactical level.
Chapter 24 A review of the use of computational intelligence in the design of
military surveillance networks” surveys the state of the art in the application of CI
methods to design various types of sensor networks, including wireless/fixed sensor,
mobile ad hoc and cellular networks, as these constitute the backbone for realiz-
ing Intelligence, Surveillance and Reconnaissance (ISR) military operations. The
authors also list important defense and security applications of these networked sys-
tems, review the CI methods and their usage and outline a number of research chal-
lenges and future directions.
Given the prolific number of sensing modalities available nowadays, determin-
ing on which platform a sensor should be mounted to collect measurements dur-
ing the next observation period is far from being a trivial task. Chapter 25 Sensor
resource management: intelligent multi-objective modularized optimization method-
ology and models” proposes a new sensor tasking framework named OPTIMA that
aims at solving this problem. OPTIMA features a Sensor Resource Analyzer mod-
ule and a Sensor Tasking Algorithm (Tasker) module. The latter leans on multiob-
jective evolutionary optimization methods to consider timing constraints, resolution
and geometric differences among the sensors with the goal of fulfilling some tasking
requirements related to maximizing the available sensor resources for search, opti-
mizing sensor resources for tracking and better defending the high-priority assets.
Chapter 26 entitled “Bio-inspired topology control mechanism for unmanned
underwater vehicles” addresses the problem of having a group of unmanned under-
water vehicles (UUVs) cooperatively self-organize in order to protect valued assets
in unknown 3D underwater spaces. The topology control mechanism is rooted in
particle swarm optimization and employs Yao-graph-inspired metrics to craft its
fitness function. The self-organization protocol only requires neigborhood-limited
UUV information to collectively guide the UUVs to make movement decisions in
these unknown 3D spaces. The algorithm is able to provide a user-defined level of
protection for different maritime vessel applications. The proposed methodology is
illustrated with three examples: (1) uniform coverage of the underside of a mar-
itime vessel; (2) plane formation to cover a given dimension in the 3D space and
(3) forming a sphere around a given asset such as a fully submerged submarine while
maintaining connectivity.
Our hope is that the wealth of technical contributions gathered in this book helps
create further momentum and drive forward many other theoretical and practical
aspects of the fascinating synergy between CI methods and the defense and security
problem spaces. Enjoy the reading!
Recent Advances in Computational Intelligence in Defense and Security 9
1. Barfield, W., Dingus, T.A.: Human Factors in Intelligent Transportation Systems. Psychology
Press, New York (2014)
2. Hong, J., Baker, M.: Wearable computing. IEEE Pervasive Comput. 2, 7–9 (2014)
3. Kacprzyk, J., Pedrycz, W.: Springer Handbook of Computational Intelligence. Springer, New
York (2015)
4. Kruse, R., Borgelt, C., Klawonn, F., Moewes, C., Steinbrecher, M., Held, P.: Computational
Intelligence: a Methodological Introduction. Springer Science & Business Media, Berlin (2013)
5. Lu, G., Zeng, W.H.: Cloud computing survey. In: Applied Mechanics and Materials, vol. 530,
pp. 650–661. Trans Tech Publ (2014)
6. Perera, C., Zaslavsky, A., Christen, P., Georgakopoulos, D.: Context aware computing for the
internet of things: a survey. Commun. Surv. Tutor. IEEE 16(1), 414–454 (2014)
7. Verdone, R., Dardari, D., Mazzini, G., Conti, A.: Wireless Sensor and Actuator Networks: Tech-
nologies, Analysis and Design. Academic Press, London (2010)
ResearchGate has not been able to resolve any citations for this publication.
Full-text available
To give you a preview of IEEE Pervasive Computing's October-December special issue on wearable computing, this installment of Notes from the Community focuses on submissions about wearables. The topics range from the origins of wearable computing to unusual examples of wearables, and from emerging uses to wearables in popular culture.
Full-text available
As we are moving towards the Internet of Things (IoT), the number of sensors deployed around the world is growing at a rapid pace. Market research has shown a significant growth of sensor deployments over the past decade and has predicted a significant increment of the growth rate in the future. These sensors continuously generate enormous amounts of data. However, in order to add value to raw sensor data we need to understand it. Collection, modelling, reasoning, and distribution of context in relation to sensor data plays critical role in this challenge. Context-aware computing has proven to be successful in understanding sensor data. In this paper, we survey context awareness from an IoT perspective. We present the necessary background by introducing the IoT paradigm and context-aware fundamentals at the beginning. Then we provide an in-depth analysis of context life cycle. We evaluate a subset of projects (50) which represent the majority of research and commercial solutions proposed in the field of context-aware computing conducted over the last decade (2001-2011) based on our own taxonomy. Finally, based on our evaluation, we highlight the lessons to be learnt from the past and some possible directions for future research. The survey addresses a broad range of techniques, methods, models, functionalities, systems, applications, and middleware solutions related to context awareness and IoT. Our goal is not only to analyse, compare and consolidate past research work but also to appreciate their findings and discuss their applicability towards the IoT.
Since the concept of cloud computing was proposed in 2006, cloud computing has been considered as the technology that probably drives the next-generation Internet revolution and rapidly becomes the hottest topic in the field of IT. The paper synthetically introduces cloud computing techniques, including the currently non-uniform definition and the characteristics of cloud computing; The paper also introduces the core techniques of cloud computing, such as data management techniques, data storage techniques, programming model and virtualization techniques. Then the 4-tie overall technique framework of general cloud computing is talked about. Finally, the paper talks about the obstacles and opportunities.
Wireless Sensor and Actuator Networks: Technologies, Analysis and Design
  • R Verdone
  • D Dardari
  • G Mazzini
  • A Conti
Verdone, R., Dardari, D., Mazzini, G., Conti, A.: Wireless Sensor and Actuator Networks: Technologies, Analysis and Design. Academic Press, London (2010)