Secure and Energy Concise Route Revamp Technique in Wireless Sensor Networks

Energy conservation has become a significant consideration in wireless sensor networks(WSN).In the sensor network,the sensor nodes have internal batteries,and as a result,they expire after a certain period.As a result,expanding the life duration of sensing devices by improving data depletion in an effective and sustainable energy-efficient way remains a challenge.Also,the clustering strategy employs to enhance or extend the life cycle of WSNs.We identify the supervisory head node(SH)or cluster head(CH)in every grouping considered the feasible strategy for power-saving route discovery in the clustering model,which diminishes the communication overhead in the WSN.However,the critical issue was determining the best SH for ensuring timely communication services.Our secure and energy concise route revamp technology(SECRET)protocol involves selecting an energy-concise cluster head(ECH)and route revamping to optimize navigation.The sensors transmit information over the ECH,which delivers the information to the base station via the determined optimal path using our strategy for effective data transmission.We modeled our methods to accom-plish power-efficient multi-hop routing.Furthermore,protected navigation helps to preserve energy when routing.The suggested solution improves energy savings,packet delivery ratio(PDR),route latency(RL),network lifetime(NL),and scalability.

Location-Based Routing Algorithms for Wireless Sensor Network

Routing algorithms based on geographical location is an important research subject in the Wireless Sensor Network(WSN).They use location information to guide routing discovery and maintenance as well as packet forwarding,thus enabling the best routing to be selected,reducing energy consumption and optimizing the whole network.Through three aspects involving the flooding restriction scheme,the virtual area partition scheme and the best routing choice scheme,the importance of location information is seen in the routing algorithm.

Graph partitioning algorithm for opportunistic routing in large-scale wireless network

Opportunistic routing takes advantage of the broadcast nature of wireless communications by forwarding data through a set of opportunistic paths instead of one ＇best＇ path in traditional routing. However, using the global scheduling opportunistic scheme like the existing opportunistic routing protocol （ExOR） would consume considerable transmission latency and energy in large-scale wireless topologies. In this article, a graph partitioning algorithm is proposed, namely, minimum cut with laplacians （MCL）, to divide the Ad-hoc network topology into subgraphs with minimized edge cuts across them. Then the existing opportunistic routing can be applied locally in each subgraph. In this way, forwarders in different subgraphs can transmit simultaneously, and each node only needs to maintain a local forwarder list instead of a global one. The simulations show that using MCL scheme in the opportunistic routing can reduce the end-to-end delay by about 49%, and increase the life time of the wireless node by about 39%.

Improved ant colony-based multi-constrained QoS energy-saving routing and throughput optimization in wireless Ad-hoc networks

In order to establish a route supporting multi-constrained quality of service（QoS）, increase network throughput and reduce network energy consumption, an improved ant colony-based multi-constrained QoS energy-saving routing algorithm（IAMQER） is proposed. The ant colony algorithm, as one of the available heuristic algorithms, is used to find the optimal route from source node to destination node. The proposed IAMQER algorithm, which is based on the analysis of local node information such as node queue length, node forwarding number of data packets and node residual energy, balances the relationship between the network throughput and the energy consumption, thus improving the performance of network in multi-constrained QoS routing. Simulation results show that this IAMQER algorithm can find the QoS route that reduce average energy consumption and improves network packet delivery ratio under the end-to-end delay and packet loss ratio constraints.

Clustering routing algorithm of self-energized wireless sensor networks based on solar energy harvesting

Aiming at the problems of existing clustering routing algorithm of self-energized wireless sensor networks（WSNs） on fixed threshold for resurrection, incapacitates reappoint cluster head in the next round and lack of election limit, this paper proposes a novel clustering routing algorithm for self-energized WSNs clustering routing algorithm based on solar energy harvesting（CRBS） algorithm. The algorithm puts forward a threshold sensitive resurrection mechanism, reviving the node when harvesting energy reaches the set soft or hard energy threshold. Meanwhile, combined with current energy harvesting level, cluster head node can decide whether to reappoint the cluster head in the next round. What＇s more, CRBS optimizes the cluster head election threshold to limit the incompetent node in election. Combined with the solar energy harvesting simulation, the results show that CRBS algorithm can better keep the default cluster head proportion, and outperforms energy balanced clustering with self-energization（EBCS） algorithm in terms of surviving nodes number and the success ratio of data transmission

Joint Design of Clustering and In-cluster Data Route for Heterogeneous Wireless Sensor Networks

A heterogeneous wireless sensor network comprises a number of inexpensive energy constrained wireless sensor nodes which collect data from the sensing environment and transmit them toward the improved cluster head in a coordinated way. Employing clustering techniques in such networks can achieve balanced energy consumption of member nodes and prolong the network lifetimes.In classical clustering techniques, clustering and in-cluster data routes are usually separated into independent operations. Although separate considerations of these two issues simplify the system design, it is often the non-optimal lifetime expectancy for wireless sensor networks. This paper proposes an integral framework that integrates these two correlated items in an interactive entirety. For that,we develop the clustering problems using nonlinear programming. Evolution process of clustering is provided in simulations. Results show that our joint-design proposal reaches the near optimal match between member nodes and cluster heads.