Evolutionary multi-path routing for network lifetime and robustness in wireless sensor networks

Ad hoc networks are wireless mobile networks that can operate without infrastructure and without centralized network management. Traditional techniques of routing are not well adapted. Indeed, their lack of reactivity with respect to the variability of network changes makes them difficult to use. Moreover, conserving energy is a critical concern in the design of routing protocols for ad hoc networks, because most mobile nodes operate with limited battery capacity, and the energy depletion of a node affects not only the node itself but also the overall network lifetime. In all proposed single-path routing schemes a new path-discovery process is required once a path failure is detected, and this process causes delay and wastage of node resources. A multipath routing scheme is an alternative to maximize the network lifetime. In this paper, we propose an energy-efficient multipath routing protocol, called AOMR-LM (Ad hoc On-demand Multipath Routing with Lifetime Maximization), which preserves the residual energy of nodes and balances the consumed energy to increase the network lifetime. To achieve this goal, we used the residual energy of nodes for calculating the node energy level. The multipath selection mechanism uses this energy level to classify the paths. Two parameters are analyzed: the energy threshold beta and the coefficient alpha. These parameters are required to classify the nodes and to ensure the preservation of node energy. Our protocol improves the performance of mobile ad hoc networks by prolonging the lifetime of the network. This novel protocol has been compared with other protocols: AOMDV and ZD-AOMDV. The protocol performance has been evaluated in terms of network lifetime, energy consumption, and end-to-end delay.

It is important to prolong the network lifetime in Wireless Sensor Networks. There are two approaches to increase the network lifetime, which are balancing energy consumption and reducing the number of non-working nodes. This paper presents two Cluster head Selection algorithms, one is based on Ensuring target Area and the other is based on Affinity Propagation. We propose a method which makes the energy uniformly distributed throughout the network nodes. In particular, we analyse the effect of the powerful nodes on the network lifetime and cluster characteristics. Simulation results show that the two proposed algorithms distribute non-working nodes evenly, such that the target area is well covered by the remaining nodes. Our proposed algorithms balance the traffic load and energy distributions, and prolong the network lifetime effectively compared with the existing schemes.

Sensor nodes are deployed in remote wireless sensor networks (WSNs) which are mainly constricted by battery life and communication range. Single path routing mechanisms in WSNs leads to drastic power utilization and path failure. A multipath routing scheme using cooperative neighboring node concept is presented in this paper. Based on the availability of the energy at the neighboring nodes, multipath routes are recognized. Our simulation results demonstrate that the proposed scheme enhances the performance in terms of multipath identification delay, end-to-end delay, packet delivery ratio and network life time etc.

A wireless sensor network (WSN) comprised of a plurality of sensor nodes powered by their internal batteries and one or more base stations. Efficient utilization of the energy resources leads to prolonged network lifetime. In this study, the effects of limiting the total hop count within the network on network lifetime is investigated by using a mixed integer programming (MIP) framework. Our results show that when the hop count of network is limited by minimum hop count, lifetime of network can decrease by %40 of the lifetime obtained without any constraint on hop count. If hop count limit of network is increased by %10, lifetime of network can reach %98 of maximum lifetime obtained without any restrictions on hop count.

Maximization of lifetime of wireless sensor network (WSN) is an emerging area of research in present scenario. Many authors are performing their research work, so that they could achieve lower energy consumption for the sensor network which leads to increase in lifetime of network. Our work focuses on sink mobility in a fixed trajectory within a wireless sensor network while sensed data are required to be collected. We divide the whole sensor network into two different regions as direct communication area (DCA) and multi-hop communication area (MCA). Sensors node that lies in DCA is at one hop count distance from the sink node trajectory while sensors within MCA are at more than one hop count from the trajectory. We considered all the sensors that are within DCA as subsink nodes. During sink mobility, whenever a subsink is closer to the mobile sink node, then it starts transmitting its data to the sink node. But those sensors that are within MCA needs to search an appropriate sink node for sending its sensed data to the subsink so that on further stage, that subsink could provide those data to the sink node whenever it is in nearest proximity of that subsink. Basically, a sink node is the ultimate destination for the data while a subsink node acts as a relay node for the nodes that are within MCA. So our work is to find out the appropriate subsink node from a given set of subsinks. For doing so, we used location-aided routing on global positioning system (GPS)-enabled sensors and sink node. Our work is validated through simulation experiments using NS-2.

Many infrastructure networks, such as power grids, road networks, and the Internet, are embedded in space. These networks are extremely susceptible to geographically localized attacks caused by natural disasters and will experience cascading overload failures due to node load redistribution. Analyzing the robustness of these spatial networks under extreme geographically localized attacks (EGLAs), in which attacks target the most critical locations of networks, is important for improving their safety. However, existing studies for analyzing the robustness of spatial networks under EGLA only consider the initial failures caused by attacks, ignoring the effect of cascading overload failures. In this paper, we simultaneously consider the effects of the above factors to study the robustness of networks with loads under EGLA. We construct a strategy to identify the locations of the EGLA by maximizing the importance of nodes in the attack regions and propose an adaptive simulated annealing algorithm to solve it. Experiments in the synthetic and real-world networks show that our strategy can find the locations of attacks more accurately compared to traditional methods. Remarkably, we find that the link length distribution of the network can affect the robustness of the network by influencing the betweenness distribution of the nodes. Accordingly, a strategy by randomly adding long-range edges is designed to improve the robustness of real infrastructure networks, which can provide new insights into the safety of systems.

We analyze the impact of the structure of the Data Gathering (DG) trees on node lifetime (round of first node failure) and network lifetime (minimum number of rounds by which the network gets either disconnected due to node failures or the fraction of coverage loss reaches a threshold) in wireless sensor networks through extensive simulations. The two categories of DG trees studied are: the Bottleneck Node Weight-Based (BNW-DG) trees and Bottleneck Link Weight-Based (BLW-DG) trees. The BNW-DG trees incur a smaller diameter and a significantly larger fraction of nodes as leaf nodes: thus, protecting a majority of the nodes in the network from simultaneously being exhausted of the energy resources (contributing to a significantly larger network lifetime); nevertheless the nodes that serve as intermediate nodes in the first few instances of the BNW-DG trees are bound to lose their energy more quickly than the other nodes, leading to a smaller node lifetime compared to that of the BLW-DG trees (that incur a larger diameter and a relatively lower fraction of nodes as leaf nodes).

Using the mobile sink for data collection from all sensors is the most highly effective way to prolong the network lifetime of wireless sensor networks(WSNs). Many studies focused on the anchors selection, aiming to reduce the path length of mobile sink and prolong the network lifetime. However, the appropriateness and the number of the selected anchors, which significantly impact the network lifetime of the entire WSNs, still can be improved. This paper proposes a Dynamic Anchor Points Mechanism for data collection, called DAM, aiming to select appropriate anchors under the path length constraint for prolonging the network lifetime. The experimental results show that the proposed DAM outperforms existing data collection mechanisms in term of network lifetime and fairness index.

Network robustness optimization is crucial for enhancing the resilience of industrial networks and social systems against malicious attacks. Existing studies typically evaluate the robustness by simulating the sequential removal of nodes or edges and recording the residual connectivity at each step. However, the attack simulation is computationally expensive and becomes impractical for large-scale networks. Therefore, this paper proposes a multiobjective evolutionary algorithm assisted by a graph isomorphism network (GIN)-based surrogate model to efficiently optimize network robustness. First, the robustness optimization task is formulated as a multiobjective problem that simultaneously considers network robustness against attacks and the structural modification cost. Then, a GIN-based surrogate model is constructed to approximate the robustness, replacing the expensive simulation assessments. Finally, the multiobjective evolutionary algorithm is employed to explore promising network structures guided by the surrogate model, which is continuously updated via online learning to improve both prediction accuracy and optimization performance. Experimental results in various synthetic and real-world networks demonstrate that the proposed algorithm reduces the computational cost of the robustness evaluation by about 65% while achieving comparable or even superior robustness optimization performance compared with those of baseline algorithms. These results indicate that the proposed method is practical and scalable and can be applied to enhance the robustness of industrial and social networks.

SummaryEnergy consumption in wireless sensor network (WSN) is one of the important issues as tiny sensor nodes are equipped with a non‐replaceable batteries. Clustering is one of the effective means to prolong the network lifetime in WSN by reducing the number of packets to be sent from cluster heads (CHs) to base station (BS). Recently, game theory techniques have been applied in the clustering process for optimal CH election at each round. However, none of them have considered the energy parameter in their game strategy and multihop routing for data transmission. In this manuscript, an energy‐efficient game‐theoretic clustering and multi‐hop routing scheme (EGCR) has been proposed where the non‐cooperative approach of game theory is used to elect optimal CHs. In the proposed game theoretic framework, each node behaves in a selfish manner while considering the residual energy of nodes and focuses on increasing the overall lifetime of the network. Nash equilibrium is determined among a set of pure and mixed strategies, and then the expected payoff of a node is determined for Nash equilibria. Apart from the energy‐efficient clustering, the EGCR also proposes a multi‐hop routing solution for data transmission that further enhances the lifetime of whole network. In the multi‐hop routing approach, inter‐cluster and intra‐cluster routing techniques are proposed to identify optimal routes from nodes to CHs and from CHs to BS by using various factors such as node centrality, distance from the node to BS, and residual energy. The proposed algorithm is compared with traditional clustering schemes like low‐energy adaptive clustering hierarchy (LEACH) and efficient energy‐aware game theory‐based clustering (EEGC) and also with existing game theory‐based algorithms such as clustered routing for selfish sensors (CROSS), localized game theoretical clustering algorithm (LGCA), and energy‐efficient clustering algorithm based on game theory (ECAGT). Simulation experiments validate that the proposed algorithm enhances the network's lifetime up to 47.49% compared to existing clustering algorithms.

Extensive research is being done on Wireless sensor Networks (WSNs) in present decade. WSNs consist of large number of sensor nodes which perform sensing of certain phenomena and send the sensed data to the sinks. This operation consumes energy of sensor nodes which effects the lifetime of a network. To prolong the lifetime of WSNs various strategies and routing algorithms have been proposed. Use of mobile sinks has proved effective in this respect. In this paper we are analyzing the impact of multiple mobile sinks with proactive hierarchical clustering protocols LEACH and network reactive hierarchical protocol TEEN. This paper will give simulation based analysis of using multiple mobile sinks in network and their effect on lifetime of a network. Performance analysis will be done on basis of dead nodes, alive nodes, throughput and residual energy.

Clustering is one of the efficient techniques which not only help in protraction of lifetime of wireless sensor network but also make it scalable and robust. Subdivision of network into group of sensor nodes with a coordinator is called a cluster. Cluster members collect the physical data by sensing the environment and forward it to the coordinator which is generally termed as Cluster Head (CH). This cluster head aggregates the data and transmit it to the Base Station for further processing of data to meet the requirement of the application. In this paper we have proposed a novel self organising clustering scheme which considers the real time parameters for setting up the clusters for data collection. Unlike several proposed algorithm, this scheme reclusters the network only when CH fall below a threshold level. Repeated unnecessary clustering in every round depletes the energy of the network more quickly. We have introduced heterogeneity in the proposed work. By virtue of heterogeneity in terms of energy, lifetime of the network can be extended. An algorithm is functional if the area of interest is covered by active nodes. The period for which the network is functional is termed as persistent period in our paper. Simulation results show that the proposed scheme is comparatively more energy efficient, scalable & robust and has longer persistent period.

An optimized node-disjoint multipath routing scheme in mobile ad hoc

Division of the entire wireless sensor network geographic area into number of equal sized regions by applying virtual grid and strategy to appoint head node of the cluster in each region improves stability, consistency, and lifetime of the wireless sensor network. Applying the same size of grid to the different size of wireless sensor networks will not give consistent performance. In this paper, we are presenting a protocol which will dynamically determine the size of the virtual grid, which will give optimal performance of any geographic sized wireless sensor network. The protocol also allows to place sink node to any location and not at some fixed specific location. Protocol discussed in this paper can also support multiple sink nodes. Protocol dynamically takes decision to transmit the data directly to the sink node or by using multi-hop transmission by considering distances between sensor nodes internally and sink node(s), which will optimize energy efficiency of the sensor nodes and increases the lifetime of wireless sensor network.

Wireless sensor network is an emerging technology capable of having low-cost and low-power and battery-powered sensor node. At this point the protocol concentric clustered power-efficient gathering in sensor information systems and clustered general self organized tree based energy-balance routing protocol energy efficient protocol are used to increase the energy and to extend network lifetime. For these achievement of clustering techniques, optimization technique particle swarm optimization is used for data transmission through base station. If the base station is inaccessible region from the cluster-head, energy consumption will be augmented and it can shrink the lifetime of wireless sensor network. To conquer these, multi-hop gateway node is implemented with both protocol to achieve maximum lifetime and energy efficiency of wireless sensor network. To improve the quality-of-service (QoS) placing more number of gateways is required. So we propose a new algorithm nearer optimal algorithm which recursively computes least possible adequate dominating sets, at the time constantly preserving QoS requirements across iterations. In this paper comparison is done in terms of throughput, packets delivery ratio and number of nodes alive.