A Coverage-Aware Unequal Clustering Protocol with Load Separation for Ambient Assisted Living Based on Wireless Sensor Networks

Ambient Assisted Living(AAL) is becoming an important research field. Many technologies have emerged related with pervasive computing vision, which can give support for AAL. One of the most reliable approaches is based on wireless sensor networks(WSNs). In this paper, we propose a coverage-aware unequal clustering protocol with load separation(CUCPLS) for data gathering of AAL applications based on WSNs. Firstly, the coverage overlap factor for nodes is introduced that accounts for the degree of target nodes covered. In addition, to balance the intra-cluster and inter-cluster energy consumptions, different competition radiuses of CHs are computed theoretically in different rings, and smaller clusters are formed near the sink. Moreover, two CHs are selected in each cluster for load separation to alleviate the substantial energy consumption difference between a single CH and its member nodes. Furthermore, a backoff waiting time is adopted during the selection of the two CHs to reduce the number of control messages employed. Simulation results demonstrate that the CUCPLS not only can achieve better coverage performance, but also balance the energy consumption of a network and prolong network lifetime.

Efficient Algorithm for K-Barrier Coverage Based on Integer Linear Programming

Barrier coverage of wireless sensor networks is an important issue in the detection of intruders who are attempting to cross a region of interest.However,in certain applications,barrier coverage cannot be satisfied after random deployment.In this paper,we study how mobile sensors can be efficiently relocated to achieve k-barrier coverage.In particular,two problems are studied:relocation of sensors with minimum number of mobile sensors and formation of k-barrier coverage with minimum energy cost.These two problems were formulated as 0–1 integer linear programming(ILP).The formulation is computationally intractable because of integrality and complicated constraints.Therefore,we relax the integrality and complicated constraints of the formulation and construct a special model known as RELAX-RSMN with a totally unimodular constraint coefficient matrix to solve the relaxed 0–1 ILP rapidly through linear programming.Theoretical analysis and simulation were performed to verify the effectiveness of our approach.

The Coverage Holes of The Largest Component of Random Geometric Graph

In this paper, a domain in a cube is called a coverage hole if it is not covered by the largest component of the random geometric graph in this cube. We obtain asymptotic properties of the size of the largest coverage hole in the cube. In addition, we give an exponentially decaying tail bound for the probability that a line with length s do not intersect with the coverage of the infinite component of continuum percolation. These results have applications in communication networks and especially in wireless ad-hoc sensor networks.

Harris Hawks Algorithm Incorporating Tuna Swarm Algorithm and Differential Variance Strategy

Because of the low convergence accuracy of the basic Harris Hawks algorithm,which quickly falls into the local optimal,a Harris Hawks algorithm combining tuna swarm algorithm and differential mutation strategy(TDHHO)is proposed.The escape energy factor of nonlinear periodic energy decline balances the ability of global exploration and regional development.The parabolic foraging approach of the tuna swarm algorithm is introduced to enhance the global exploration ability of the algorithm and accelerate the convergence speed.The difference variation strategy is used to mutate the individual position and calculate the fitness,and the fitness of the original individual position is compared.The greedy technique is used to select the one with better fitness of the objective function,which increases the diversity of the population and improves the possibility of the algorithm jumping out of the local extreme value.The test function tests the TDHHO algorithm,and compared with other optimization algorithms,the experimental results show that the convergence speed and optimization accuracy of the improved Harris Hawks are improved.Finally,the enhanced Harris Hawks algorithm is applied to engineering optimization and wireless sensor networks(WSN)coverage optimization problems,and the feasibility of the TDHHO algorithm in practical application is further verified.