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TSDDR: Threshold Sensitive Density Controlled Divide and Rule Routing Protocol for Wireless Sensor Networks
Abstract
In Wireless Sensor Networks, efficient energy man-agement is of great importance. In this paper, we propose a novel routing protocol; Threshold Sensitive Density Controlled Divide and Rule (TSDDR) to prolong network lifetime and stability period. To achieve these targets, we utilize static clustering with threshold aware transmissions. Simulations are done in MATLAB and the results show that our protocol has 60% longer stability period than LEACH [1] and 36% longer stability period than DDR [2]. We also implemented the Uniform Random Model (URM) to find Packet Drop to make our scheme more practical.
... To tackle this, many protocols recommended clustering of the network area, a pioneer contribution by W. B. Heinzelman [7]. Fundamentally, clustering divides the field into multiple smaller observation versions thereby making resource management a comparatively convenient task [8]- [11]. However, this requires free and fair election of cluster heads (CHs) in each cluster. ...
... To optimize resources, a sensible decision is to deploy an equal percentage of nodes over different regions to ensure minimization of coverage holes, and elongation of network lifetime. Therefore, in this scenario, we propose to deploy 20% of the nodes in region R 1 and the rest 80% of the nodes to be distributed evenly over R 2,3,..., 8,9 regions as shown in Fig. 3(a). This nodes' deployment always depend upon the network field area and number of nodes. ...
... Note that even though the CH of R 3 is receiving data from CHs of both R 7 and R 8 , this is blessing in disguise. This is because, as shown in the figure, the CHs of both R 7 and R 8 have not received data from all the nodes in its region, since some nodes find another nearest CH, so these CHs are aggregating ...
... To tackle the aforementioned problem, many protocols have exploited and voted for clustering of the network area, a pioneer contribution by W. B. Heinzelman [12], as one of the appreciated methods toward efficient resource utilization. Fundamentally, clustering aims to effectively divide the network field into multiple smaller observation versions thereby making resource management a comparatively convenient task [13]- [15]. However, this requires free and fair election of cluster heads (CHs) in each cluster. ...
... Without loss of generality, let ρ and δ be positive real numbers because for ρ < 0, P would be replaced by −P , and then we would write | ρ | instead of ρ. The commutative probability function can be written as: (13) where ϕ(.) represents the unit Gaussian density function. However, as the integrand (2π) −1 exp(−(p 2 + q 2 )/2) possesses circular symmetry, the numerical property of this in- tegral is a function of length of the origin from ρp + δq = z . ...
In this paper, we combat the problem of performance optimization in wireless sensor networks. Specifically, a novel framework is proposed to handle two major research issues. Firstly, we optimize the utilization of resources available to various nodes at hand. This is achieved via proposed optimal network clustering enriched with layer-adaptive 3-tier communication mechanism to diminish energy holes. We also introduce a mathematical coverage model that helps us minimize the number of coverage holes. Secondly, we present a novel approach to recover the corrupted version of the data received over noisy wireless channels. A robust sparse-domain based recovery method equipped with specially developed averaging filter is used to take care of the unwanted noisy components added to the data samples. Our proposed framework provides a handy routing protocol that enjoys improved computation complexity and elongated network lifetime as demonstrated with the help of extensive simulation results.
... To tackle this, many protocols recommended clustering of the network area, a pioneer contribution by W. B. Heinzelman [13]. Fundamentally, clustering divides the field into multiple smaller observation versions thereby making resource management a comparatively convenient task [14][15][16][17]. However, this requires free and fair election of cluster heads (CHs) in each cluster. ...
In this paper, we propose a novel framework for performance optimization in Internet of Things (IoT)-based next-generation wireless sensor networks. In particular, a computationally-convenient system is presented to combat two major research problems in sensor networks. First is the conventionally-tackled resource optimization problem which triggers the drainage of battery at a faster rate within a network. Such drainage promotes inefficient resource usage thereby causing sudden death of the network. The second main bottleneck for such networks is that of data degradation. This is because the nodes in such networks communicate via a wireless channel, where the inevitable presence of noise corrupts the data making it unsuitable for practical applications. Therefore, we present a layer-adaptive method via 3-tier communication mechanism to ensure the efficient use of resources. This is supported with a mathematical coverage model that deals with the formation of coverage holes. We also present a transform-domain based robust algorithm to effectively remove the unwanted components from the data. Our proposed framework offers a handy algorithm that enjoys desirable complexity for real-time applications as shown by the extensive simulation results.
... To achieve these targets, it utilized static clustering with threshold aware transmissions. Simulations were done in MATLAB and the results showed that the protocol has 60% longer stability period than LEACH and 36% longer stability period than DDR [1]. ...
In Wireless Sensor Networks, efficient energy management is always considered as one of the most important part. In this paper, we have proposed a routing protocol; Energy Efficient Three Tire Routing Protocol (EETTRP) for increasing the overall life time of the network. By utilizing Threshold Distributed Energy mechanism and Three Tier Communication Protocol is derived. Simulations are done in MATLAB and the results show that our protocol has longer stability than DEEC, DDEEC and EDEEC as well as much data transmission rate than DEEC, DDEEC and EDEEC.
... We assume the commonly used simple first order radio model [24]. The radio parameters for our model are shown in table 1. ...
In this paper, we propose a novel framework for performance optimization in Internet of Things (IoT)-based next-generation wireless sensor networks. In particular, a computationally-convenient system is presented to combat two major research problems in sensor networks. First is the conventionally-tackled resource optimization problem which triggers the drainage of battery at a faster rate within a network. Such drainage promotes inefficient resource usage thereby causing sudden death of the network. The second main bottleneck for such networks is the data degradation. This is because the nodes in such networks communicate via a wireless channel, where the inevitable presence of noise corrupts the data making it unsuitable for practical applications. Therefore, we present a layer-adaptive method via 3-tier communication mechanism to ensure the efficient use of resources. This is supported with a mathematical coverage model that deals with the formation of coverage holes. We also present a transform-domain based robust algorithm to effectively remove the unwanted components from the data. Our proposed framework offers a handy algorithm that enjoys desirable complexity for real-time applications as shown by the extensive simulation results.
KeywordsCoverage holesDenoisingEnergy efficiencyEnergy holesSparse representationsWireless sensor networks
The use of mobile sensors is of great relevance to monitor hazardous applications where sensors cannot be deployed manually. Traditional algorithms primarily aim at maximizing network coverage rate, which leads to the creation of the "energy hole" in the region near the sink node. In this article, we are addressing the problem of redistributing mobile sensor nodes over an unattended target area. Driven by energy efficiency considerations, a pixel-based transmission scheme is developed to reduce extra overhead caused by frequent sensing and decision making. We derive the optimal node distribution and provide a theoretical explanation of balanced energy depletion for corona-based sensor network. In addition, we demonstrate that it can be extended to deal with uneven energy depletion due to the many-to-one communications in multi-hop wireless sensor networks. Applying the optimal condition, we then propose a novel sensor redistribution algorithm to completely eliminate the energy hole problem in mobile sensor network. Extensive simulation results verify that the proposed solution outperforms others in terms of coverage rate, average moving distance, residual energy, and total energy consumption.
We study the impact of heterogeneity of nodes, in terms of their energy, in wireless sensor networks that are hierarchically clustered. In these networks some of the nodes become cluster heads, aggregate the data of their cluster members and transmit it to the sink. We assume that a percentage of the population of sensor nodes is equipped with additional energy resources—this is a source of heterogeneity which may result from the initial setting or as the operation of the network evolves. We also assume that the sensors are randomly (uniformly) distributed and are not mobile, the coordinates of the sink and the dimensions of the sensor field are known. We show that the behavior of such sensor networks becomes very unstable once the first node dies, especially in the presence of node heterogeneity. Classical clustering protocols assume that all the nodes are equipped with the same amount of energy and as a result, they can not take full advantage of the presence of node heterogeneity. We propose SEP, a heterogeneous-aware protocol to prolong the time interval before the death of the first node (we refer to as stability period), which is crucial for many applications where the feedback from the sensor network must be reliable. SEP is based on weighted election probabilities of each node to become cluster head according to the remaining energy in each node. We show by simulation that SEP always prolongs the stability period compared to (and that the average throughput is greater than) the one obtained using current clustering protocols. We conclude by studying the sensitivity of our SEP protocol to heterogeneity parameters capturing energy imbalance in the network. We found that SEP yields longer stability region for higher values of extra energy brought by more powerful nodes.
In order to prolong the network lifetime, energy-efficient protocols should be designed to adapt the characteristic of wireless sensor networks. Clustering Algorithm is a kind of key technique used to reduce energy consumption, which can increase network scalability and lifetime. This paper studies the performance of clustering algorithm in saving energy for heterogeneous wireless sensor networks. A new distributed energy-efficient clustering scheme for heterogeneous wireless sensor networks is proposed and evaluated. In the new clustering scheme, cluster-heads are elected by a probability based on the ratio between residual energy of node and the average energy of network. The high initial and residual energy nodes will have more chances to be the cluster-heads than the low energy nodes. Simulational results show that the clustering scheme provides longer lifetime and higher throughput than the current important clustering protocols in heterogeneous environments.
Cluster based routing technique is most popular routing technique in Wireless Sensor Networks (WSNs). Due to varying need of WSN applications efficient energy utilization in routing protocols is still a potential area of research. In this research work we introduced a new energy efficient cluster based routing technique. In this technique we tried to overcome the problem of coverage hole and energy hole. In our technique we controlled these problems by introducing density controlled uniform distribution of nodes and fixing optimum number of Cluster Heads (CHs) in each round. Finally we verified our technique by experimental results of MATLAB simulations.
The demand for maximum network lifetime in many mission-critical applications of wireless sensor networks motivates the great significance to deploy as few sensors as possible to achieve the expected network performance. In this paper, we first characterize the energy consumption of wireless sensor networks with adjustable transmission ranges through theoretical analysis. Based on this result, we propose a deployment strategy with T as the required minimum network lifetime. We come up with three interventions: (A) in order to achieve an evenly balanced energy consumption among all nodes, the node density in different areas of the network should be a continuous varying function of the distance from the sink; (B) if there are insufficient nodes to achieve a balanced energy consumption over the whole network, our proposed node deployment strategy can be used to achieve the required lifetime threshold T with minimum number of nodes; and (C) when there are sufficient nodes to ensure the network connectivity and coverage with the node density of τ, we design an algorithm to identify the optimal transmission radius r and the corresponding achievable maximum network lifetime. Our conclusions are verified by extensive simulation results.
Wireless distributed sensor network consists of randomly deployed sensors
having low energy assets. These networks can be used for monitoring a variety
of environments. Major problems of these networks are energy constraints and
their finite lifetimes. To overcome these problems different routing protocols
and clustering techniques are introduced. We propose DREEM-ME which uses a
unique technique for clustering to overcome these two problems efficiently.
DREEM-ME elects a fix number of cluster heads (CHs) in each round instead of
probabilistic selection of CHs. Packet Drop Technique is also implemented in
our protocol to make it more comprehensive and practical. In DREEM-ME
confidence interval is also shown in each graph which helps in visualising the
maximum deviation from original course. Our simulations and results show that
DREEM-ME is much better than existing protocols of the same nature.
In this paper, we propose Regional Energy Efficient Cluster Heads based on
Maximum Energy (REECH-ME) Routing Protocol for Wireless Sensor Networks (WSNs)
. The main purpose of this protocol is to improve the network lifetime and
particularly the stability period of the network. In REECH-ME, the node with
the maximum energy in a region becomes Cluster Head (CH) of that region for
that particular round and the number of the cluster heads in each round remains
the same. Our technique outperforms LEACH which uses probabilistic approach for
the selection of CHs. We also implement the Uniform Random Distribution Model
to find the packet drop to make this protocol more practical. We also calculate
the confidence interval of all our results which helps us to visualize the
possible deviation of our graphs from the mean value.
Wireless distributed microsensor systems will enable the reliable monitoring of a variety of environments for both civil and military applications. In this paper, we look at communication protocols, which can have significant impact on the overall energy dissipation of these networks. Based on our findings that the conventional protocols of direct transmission, minimum-transmission-energy, multi-hop routing, and static clustering may not be optimal for sensor networks, we propose LEACH (Low-Energy Adaptive Clustering Hierarchy), a clustering-based protocol that utilizes randomized rotation of local cluster based station (cluster-heads) to evenly distribute the energy load among the sensors in the network. LEACH uses localized coordination to enable scalability and robustness for dynamic networks, and incorporates data fusion into the routing protocol to reduce the amount of information that must be transmitted to the base station. Simulations show the LEACH can achieve as much as a factor of 8 reduction in energy dissipation compared with conventional outing protocols. In addition, LEACH is able to distribute energy dissipation evenly throughout the sensors, doubling the useful system lifetime for the networks we simulated.
The clustering Algorithm is a kind of key technique used to reduce energy consumption. It can increase the scalability and lifetime of the network. Energy-efficient clustering protocols should be designed for the characteristic of heterogeneous wireless sensor networks. We propose and evaluate a new distributed energy-efficient clustering scheme for heterogeneous wireless sensor networks, which is called DEEC. In DEEC, the cluster-heads are elected by a probability based on the ratio between residual energy of each node and the average energy of the network. The epochs of being cluster-heads for nodes are different according to their initial and residual energy. The nodes with high initial and residual energy will have more chances to be the cluster-heads than the nodes with low energy. Finally, the simulation results show that DEEC achieves longer lifetime and more effective messages than current important clustering protocols in heterogeneous environments.
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2000. Includes bibliographical references (p. 145-154).
Real-time wireless link reliability estimation is a fundamental building block for self-organization of multihop sensor networks. Observed connectivity at low-power is more chaotic and unpredictable than in wireless LANs, and available resources are severely constrained. We seek estimators that react quickly to large changes, yet are stable, have a small memory footprint and are simple to compute. We create a simple model that generates link loss characteristics similar to empirical traces collected under different contexts. With this model, we simulate a variety of estimators, and uses the simple exponentially weighted moving average (EWMA) estimator, as a basis for comparison. We find that recently propose flip-flop estimators are not superior. However, our cascaded EWMA on windowed averaging is very effective
In recent years, advances in energy-efficient design and wireless technologies have enabled exciting new applications for wireless devices. These applications span a wide range, including real-time and streaming video and audio delivery, remote monitoring using networked microsensors, personal medical monitoring, and home networking of everyday appliances. While these applications require high performance from the network, they suffer from resource constraints that do not appear in more traditional wired computing environments. In particular, wireless spectrum is scarce, often limiting the bandwidth available to applications and making the channel error-prone, and the nodes are battery-operated, often limiting available energy. My thesis is that this harsh environment with severe resource constraints requires an applicationspecific protocol architecture, rather than the traditional layered approach, to obtain the best possible performance. This dissertation supports this claim using d...