Abstract With the current exponential growth in traffic and service demands, device-to-device (D2D) cooperation is identified as a major mechanism to enable 5G networks to effectively and efficiently augment network resources. The effectiveness of D2D cooperation depends on a wide range of decision making processes that include cluster formation, resource allocation, in addition to connection and mobility management. Irrespective of the D2D cooperation scenario whether in sensor, ad hoc, or cellular networks, the literature normally assumes that devices selected as relays or data sources are reliable; this means that they will maintain the connection till the communication session ends. Yet, this assumption is challenged in practice since devices’ batteries can be depleted (e.g., sensors in an IoT network) and devices can move leading to connection termination (e.g., mobile users in a WiFi network or cars in a vehicular ad hoc network). To this end, we address the problem of reliable D2D cooperation in wireless networks by proposing a novel approach that is proactive by utilizing reliability metrics in the decision making process, and scalable by having low implementation complexity suitable for dense networks. These differentiating factors are shown to enhance the overall network reliability compared to standard techniques and to facilitate dynamic operation which is essential for practical implementation. Performance is evaluated using extensive simulations in addition to test bed experimental demonstration in order to quantify gains and extract insights on a range of existing design tradeoffs.
Abstract Device-to-device cooperation has emerged as a prominent solution to a wide range of challenges in large-scale wireless networks. However, the ad hoc nature of cooperative networks and their proneness to failure are a major obstacle towards their real world deployment and wide adoption. In this work, we focus on failure recovery and scalability in wireless content distribution networks with device-to-device cooperation, where a number of mobile devices in a given geographical area are interested in downloading a common content from an application service provider. We present low complexity effective algorithms based on clustering and tree construction methods in order to address three different types of dynamic node behavior, namely new devices joining the network, existing devices leaving the network, and existing devices moving locally within the network. Moreover, we propose a constrained version of the minimum spanning tree algorithm with bounds on the height of the tree and the maximum degree per node, in order to capture practical operational constraints for device-to-device cooperation in wireless networks. We present results for various network scenarios using simulations and experimental test bed to demonstrate the effectiveness of the proposed algorithms in terms of performance efficiency, computational complexity, and practical implementation feasibility. Wi-Fi Direct (WFD) has become almost a standard for peer-to-peer networking on smart devices. Typically, WFD allows a user to search for and connect to other devices, which results in creating a group of devices that can exchange data. In certain applications like alert dissemination in disaster areas, it is required to form groups dynamically and to share data across the group boundaries. However, these capabilities are not offered by the current WFD implementations. In this paper, we propose EMC, a novel protocol that allows WFD devices to dynamically cluster themselves into groups and appropriately elect group owners. EMC also allows members of distinct groups to communicate. EMC is validated through implementation on Android devices and through the realization of a chat application. Display Omitted Conservation of energy and fault tolerance are two major issues in the deployment of a wireless sensor network (WSN). Design of clustering and routing algorithms for a large scale WSN should incorporate both these issues for the long run operation of the network. In this paper, we propose distributed clustering and routing algorithms jointly referred as DFCR. The algorithm is shown to be energy efficient and fault tolerant. The DFCR uses a distributed run time recovery of the sensor nodes due to sudden failure of the cluster heads (CHs). It takes care of the sensor nodes which have no CH within their communication range. We perform extensive experiments on the proposed algorithm using various network scenarios. The experimental results are compared with the existing algorithms to demonstrate the strength of the algorithm in terms of various performance metrics. Clustering is an essential tool to facilitate the function of self-organizing peer-to-peer and sensor networks. The LEACH (Low-Energy Adaptive Clustering Hierarchy) algorithm for forming clusters in ad-hoc networks represents a probabilistic way of choosing cluster-heads which shows a large variation over the average link distances of clusters and leads to uneven energy consumption during the transmission phase. However LEACH has the advantage that the energy load of being cluster-head is distributed among all the nodes resulting in a longer network life-time. On the other hand, the RSSI (Received Signal Strength Indicator)-based algorithms try to reduce the variation over the average link distances of clusters by means of choosing cluster-heads in the areas of higher node density in order to have an even energy consumption during transmission phase. But it distributes the energy load of being the cluster-head between some specific nodes which leads to a shorter network life-time. Here, we propose a new clustering scheme E-RSSI (Enhanced RSSI), which has the main advantages of the RSSI based schemes as well as LEACH. The average link distance of E-RSSI is almost the same as the average link distance of RSSI. And E-RSSI, like RSSI, has a significantly smaller variation over the average link distances in comparison with LEACH and at the same time shows significant improvement in network life-time. E-RSSI produces a low energy cost per node like RSSI and a more evenly distributed energy cost per node like LEACH therefore a prolonged network life-time.