A New Clustering Algorithm Using Adaptive Discrete Particle Swarm Optimization in Wireless Sensor Network

Wireless sensor networks (WSNs) are mainly characterized by their limited and non-replenishable energy supply. Hence, the energy efficiency of the infrastructure greatly affects the network lifetime. Clustering is one of the methods that can expand the lifespan of the whole network by grouping the sensor nodes according to some criteria and choosing the appropriate cluster heads(CHs). The balanced load of the CHs has an important effect on the energy consumption balancing and lifespan of the whole network. Therefore, a new CHs election method is proposed using an adaptive discrete particle swarm optimization (ADPSO) algorithm with a fitness value function considering the load balancing and energy consumption. Simulation results not only demonstrate that the proposed algorithm can have better performance in load balancing than low-energy adaptive clustering hierarchy (LEACH), hybrid energy-efficient distributed clustering (HEED), and dynamic clustering algorithm with balanced load (DCBL), but also imply that the proposed algorithm can extend the network lifetime more.

Routing strategy of reducing energy consumption for underwater data collection

Underwater Wireless Sensor Networks(UWSNs)are widely used in many fields,such as regular marine monitoring and disaster warning.However,UWSNs are still subject to various limitations and challenges:ocean interferences and noises are high,bandwidths are narrow,and propagation delays are high.Sensor batteries have limited energy and are difficult to be replaced or recharged.Accordingly,the design of routing protocols is one of the solutions to these problems.Aiming at reducing and balancing network energy consumption and effectively extending the life cycle of UWSNs,this paper proposes a Hierarchical Adaptive Energy-efficient Clustering Routing(HAECR)strategy.First,this strategy divides hierarchical regions based on the depth of the sensor node in a three-dimensional(3D)space.Second,sensor nodes form different competition radii based on their own relevant attributes and remaining energy.Nodes in the same layer compete freely to form clusters of different sizes.Finally,the transmission path between clusters is determined according to comprehensive factors,such as link quality,and then the optimal route is planned.The simulation experiment is conducted in the monitoring range of the 3D space.The simulation results prove that the HAECR clustering strategy is superior to LEACH and UCUBB in terms of balancing and reducing energy consumption,extending the network lifetime,and increasing the number of data transmissions.

MimiBS： Mimicking Base-Station to Provide Location PrivacyProtection in Wireless Sensor Networks

In a wireless sensor network (WSN), sink node/base station (BS) gathers data from surrounding nodes and sends them to a remote server via a gateway. BS holds important data. Therefore, it is necessary to hide its location from an inside/outside attacker. Providing BS location anonymity against a local and global adversary, we propose a novel technique called MimiBS 'Mimicking Base-Station'. The key idea is the integration of aggregator nodes (ANs) with sensor nodes (SNs), while fine tuning TTL (time to live) value for fake packets, and setting some threshold value for real packet counter rpctr. MimiBS creates multiple traffic-hotspots (zones), which shifts the focus from BS to the newly created ANs hotspots. Multiple traffic-hotspots confuse the adversary while determining the real BS location information. We defend the BS location information anonymity against traffic analysis attack, and traffic tracing attack. MimiBS gives an illusion of having multiple BSs, and thus, if the attacker knows any about AN, he/she will be deceived between the real BS and ANs. MimiBS outperforms BLAST (base-station location anonymity and security technique), RW (random walk), and SP (shortest path), while conducting routing without fake packets, with fake packets, without energy consideration, and with energy consideration respectively.