Cellular automaton-based algorithms for the dispersion of mobile wireless sensor networks

Due to the advent of sensor technology and its applications, mobile wireless sensor networks (MWSNs) have gained a significant amount of research interest. In a typical MWSN, sensors can move within the network. We develop a set of probabilistic and deterministic cellular automaton (CA)-based algorithms for motion planning problems in MWSNs. First, we consider a scenario where a group of sensors are deployed and they need to disperse in order to maximise the area covered by the network. In this variant of the problem we do not explicitly consider that the sensors should maintain the connectivity of the network while they move. Second, we consider a scenario where the sensors are initially randomly distributed and they need to disperse autonomously to both maximise the coverage of the network and maintain its connectivity. We carry out extensive simulations of both deterministic and randomised variants of the algorithms. For the first variant of the problem we compare our algorithms with one previous algorithm and find that our algorithm yields better network coverage than the earlier algorithm. We also find that probabilistic algorithms have better overall performance for the second variant. CA algorithms rely only on local information about the network and, hence, they can be used in practice for MWSN problems. On the other hand, locality of the algorithm implies that maintaining connectivity becomes a non-trivial problem.     

An online resource allocation algorithm to minimize system interference for inband underlay D2D communications

This paper addresses the research question of total system interference minimization while maintaining a target system sum rate gain in an inband underlay device‐to‐device (D2D) communication. To the best of our knowledge, most of the state of the art research works exploit offline resource allocation algorithms to address the research problem. However, in Long‐Term Evolution (LTE) and beyond systems (4G, 5G, or 5G+), offline resource allocation algorithms do not comply with the fast scheduling requirements because of the high data rate demand. In this paper, we propose a bi‐phase online resource allocation algorithm to minimize the total system interference for inband underlay D2D communication. Our proposed algorithm assumes D2D pairs as a set of variable elements whereas takes the cellular user equipment (UEs) as a set of constant elements. The novelty of our proposed online resource allocation algorithm is that it incurs a minimum number of changes in radio resource assignment between two successive allocations among the cellular UEs and the D2D pairs. Graphical representation of the simulation results suggests that our proposed algorithm outperforms the existing offline algorithm considering number of changes in successive allocation for a certain percentage of sum rate gain maintaining the total system interference and total system sum rate very similar.     

Relax online resource allocation algorithms for D2D communication

Maximizing the system sumrate by sharing the resource blocks among the cellular user equipments and the D2D (device to device) pairs while maintaining the quality of service is an important research question in a D2D communication underlaying cellular networks. The problem can be optimally solved in offline by using the weighted bipartite matching algorithm. However, in long‐term evolution and beyond (4G and 5G) systems, scheduling algorithms should be very efficient where the optimal algorithm is quite complex to implement. Hence, a low complexity algorithm that returns almost the optimal solution can be an alternative to this research problem. In this paper, we propose 2 less complex stable matching–based relax online algorithms those exhibit very close to the optimal solution. Our proposed algorithms deal with fixed number of cellular user equipments and a variable number of D2D pairs those arrive in the system online. Unlike online matching algorithms, we consider that an assignment can be revoked if it improves the objective function (total system sumrate). However, we want to minimize the number of revocation (ie, the number of changes in the assignments) as a large number of changes can be expensive for the networks too. We consider various offline algorithms proposed for the same research problem as relaxed online algorithms. Through extensive simulations, we find that our proposed algorithms outperform all of the algorithms in terms of the number of changes in assignment between 2 successive allocations while maintaining the total system sumrate very close to the optimal algorithm.