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Recent advances in mobile communication shows proliferation in networks formed by human carried devices known as the pocket switched network (PSN). Human beings are social animals. They tend to form groups and communities, and have repetitive mobility pattern which can be used to disseminate information in PSNs. In this paper, we give a deeper insight to the nature of community formation and how such information can be used to help opportunistic forwarding in mobile opportunistic networks. Using real world mobility traces, we first derive the adjacency list for each node and form the contact graph. Using tools from social network analysis we then determine various node properties like centrality and clustering coefficient and graph properties like average path length and modularity. Based on the derived graph properties, node encounter process and nature of message dissemination in PSNs, we propose two social based routing, known as the contact based routing and community aware two-hop routing. We compare the proposed routing techniques with generic epidemic and prophet routing and Bubble-Rap, a social based routing. Results show that the proposed algorithms is able to achieve better delivery ratio and lower delay than Bubble Rap, while reducing the high overhead ratio of epidemic and prophet routing.

Security is a major concern of modern real-time applications, besides requiring stringent latency bound. However, encryption algorithms are computation intensive task which impacts the timeliness of the real-time applications. Therefore, there exists a trade-off between the desired level of security and the service guarantee. In this paper, we propose a security-aware dynamic scheduling algorithm (SADSA) using a grid of computational elements (CEs) which performs this trade-off and tries to maximize the instantaneous average security level of the packets besides providing a guaranteed service. As packets arrive, we first assign them to the CEs based on the utilization value of a CE, which is the ratio of completion time and a deadline of the last packet in a CE. The security level of all the packets is then dynamically adjusted to meet the minimum required security level while maximizing the average security level of all the packets in that CE. We first show that the proposed assignment algorithm is NP-hard, is 2-competitive to the optimal solution, and that the proposed algorithm provides a sub-optimal solution. Further, using extensive simulation, we show that the proposed SADSA algorithm performs better in terms of guarantee ratio, average security level and overall performance compared to the existing algorithms.

With an ever increasing demand for the delivery of internet video service, the service providers are facing a huge challenge to deliver ultra-HD (2k/4k) video at sub-second latency. The multi-access edge computing (MEC) platform actually helps in achieving this objective by caching popular contents at the edge of cellular network. This not only reduces the delivery latency, but also the load and the cost of the backhaul links. However, MEC platforms are afflicted by constrained resources in terms of storage and processing capabilities; and centralized caching of contents may nullify the advantage of reduced latency by lowering the offloading probability. Distributed caching at the edge not only improves the offloading probability, but also dynamically adjusts the load distribution among the MEC servers. In this article, we propose an architecture for deployment of MEC platforms by exploiting the characteristics of a scalable video encoding technique. The layered video coding techniques, such as the scalable video coding (SVC), is used by the content providers to adjust to the network dynamics, by dynamically dropping packets in order to reduce latency. We show how an SVC video easily lends itself to distributed caching at the edge. Then we investigate the latency-storage trade-off by storing the video layers at different parts of the access networks.

Real-time video surveillance has become a crucial technology for smart cities, made possible through the large-scale deployment of mobile and fixed video cameras. In this paper, we propose situation-aware streaming, for real-time identification of important events from live-feeds at the source rather than a cloud based analysis. For this, we first identify the frames containing a specific situation and assign them a high scale-of-importance (SI). The identification is made at the source using a tiny neural network (having a small number of hidden layers), which incurs a small computational resource, albeit at the cost of accuracy. The frames with a high SI value are then streamed with a certain required Signal-to-Noise-Ratio (SNR) to retain the frame quality, while the remaining ones are transmitted with a small SNR. The received frames are then analyzed using a deep neural network (with many hidden layers) to extract the situation accurately. We show that the proposed scheme is able to reduce the required power consumption of the transmitter by 38.5% for 2160p (UHD) video, while achieving a classification accuracy of 97.5%, for the given situation.

Opportunistic network is a type of Delay Tolerant Network which is characterized by intermittent connectivity amongst the nodes and communication largely depends upon the mobility of the participating nodes. The network being highly dynamic, traditional MANET protocols cannot be applied and the nodes must adhere to store-carry-forward mechanism. Nodes do not have the information about the network topology, number of participating nodes and the location of the destination node. Hence, message transfer reliability largely depends upon the mobility pattern of the nodes. In this paper we have tried to find the impact of RWP (Random Waypoint) mobility on packet delivery ratio. We estimate mobility factors like number of node encounters, contact duration(link time) and inter-contact time which in turn depends upon parameters like playfield area (total network area), number of nodes, node velocity, bit-rate and RF range of the nodes. We also propose a restricted form of RWP mobility model, called the affinity based mobility model. The network scenario consists of a source and a destination node that are located at two extreme corners of the square playfield (to keep a maximum distance between them) and exchange data packets with the aid of mobile 'helper' nodes. The source node and the destination node are static. The mobile nodes only help in relaying the message. We prove how affinity based mobility model helps in augmenting the network reliability thereby increasing the message delivery ratio and reduce message delivery latency.