Communication networks, whether they are wired or wireless, have traditionally been assumed to be connected at least most of the time. However, emerging applications such as emergency response, special operations, smart environments, VANETs, etc. coupled with node heterogeneity and volatile links (e.g. due to wireless propagation phenomena and node mobility) will likely change the typical conditions under which networks operate. In fact, in such scenarios, networks may be mostly disconnected, i.e., most of the time, end-to-end paths connecting every node pair do not exist. To cope with frequent, long-lived disconnections, opportunistic routing techniques have been proposed in which, at every hop, a node decides whether it should forward or store-and-carry a message. Despite a growing number of such proposals, there still exists little consensus on the most suitable routing algorithm(s) in this context. One of the reasons is the large diversity of emerging wireless applications and networks exhibiting such “episodic” connectivity. These networks often have very different characteristics and requirements, making it very difficult, if not impossible, to design a routing solution that fits all. In this paper, we first break up existing routing strategies into a small number of common and tunable routing modules (e.g. message replication, coding, etc.), and then show how and when a given routing module should be used, depending on the set of network characteristics exhibited by the wireless application. We further attempt to create a taxonomy for intermittently connected networks. We try to identify generic network characteristics that are relevant to the routing process (e.g., network density, node heterogeneity, mobility patterns) and dissect different “challenged” wireless networks or applications based on these characteristics. Our goal is to identify a set of useful design guidelines that will enable one to choose an appropriate routing protocol for the application or network in hand. Finally, to demonstrate the utility of our approach, we take up some case studies of challenged wireless networks, and validate some of our routing design principles using simulations.

We propose a reinforcement learning-based cell switching algorithm to minimize the energy consumption in ultra-dense deployments without compromising the quality of service (QoS) experienced by the users. In this regard, the proposed method can intelligently learn which small cells (SCs) to turn off at any given time based on the traffic load of the SCs and the macro cell. To validate the idea, we used the open call detail record (CDR) data set from the city of Milan, Italy, and tested our algorithm against typical operational benchmark solutions. With the obtained results, we demonstrate exactly when and how the proposed method can provide energy savings, and moreover how this happens without reducing QoS of users. Most importantly, we show that our solution has a very similar performance to the exhaustive search, with the advantage of being scalable and less complex.

Despite significant infrastructure improvements, cloud computing still faces numerous challenges in terms of load balancing. Several techniques have been applied in the literature to improve load balancing efficiency. Recent research manifested that load balancing techniques based on metaheuristics provide better solutions for proper scheduling and allocation of resources in the cloud. However, most of the existing approaches consider only a single or few QoS metrics and ignore many important factors. The performance efficiency of these approaches is further enhanced by merging with machine learning techniques. These approaches combine the relative benefits of load balancing algorithm backed up by powerful machine learning models such as Support Vector Machines (SVM). In the cloud, data exists in huge volume and variety that requires extensive computations for its accessibility, and hence performance efficiency is a major concern. To address such concerns, we propose a load balancing algorithm, namely, Data Files Type Formatting (DFTF) that utilizes a modified version of Cat Swarm Optimization (CSO) along with SVM. First, the proposed system classifies data in the cloud from diverse sources into various types, such as text, images, video, and audio using one to many types of SVM classifiers. Then, the data is input to the modified load balancing algorithm CSO that efficiently distributes the load on VMs. Simulation results compared to existing approaches showed an improved performance in terms of throughput (7%), the response time (8.2%), migration time (13%), energy consumption (8.5%), optimization time (9.7%), overhead time (6.2%), SLA violation (8.9%), and average execution time (9%). These results outperformed some of the existing baselines used in this research such as CBSMKC, FSALB, PSO-BOOST, IACSO-SVM, CSO-DA, and GA-ACO.

Due to the expeditious inclination of online services usage, the incidents of ransomware proliferation being reported are on the rise. Ransomware is a more hazardous threat than other malware as the victim of ransomware cannot regain access to the hijacked device until some form of compensation is paid. In the literature, several dynamic analysis techniques have been employed for the detection of malware including ransomware; however, to the best of our knowledge, hardware execution profile for ransomware analysis has not been investigated for this purpose, as of today. In this study, we show that the true execution picture obtained via a hardware execution profile is beneficial to identify the obfuscated ransomware too. We evaluate the features obtained from hardware performance counters to classify malicious applications into ransomware and non-ransomware categories using several machine learning algorithms such as Random Forest, Decision Tree, Gradient Boosting, and Extreme Gradient Boosting. The employed data set comprises 80 ransomware and 80 non-ransomware applications, which are collected using the VirusShare platform. The results revealed that extracted hardware features play a substantial part in the identification and detection of ransomware with F-measure score of 0.97 achieved by Random Forest and Extreme Gradient Boosting.

Content-Centric Networking (CCN) is a novel architecture that is shifting host-centric communication to a content-centric infrastructure. In recent years, in-network caching in CCNs has received significant attention from research community. To improve the cache hit ratio, most of the existing schemes store the content at maximum number of routers along the downloading path of content from source. While this helps in increased cache hits and reduction in delay and server load, the unnecessary caching significantly increases the network cost, bandwidth utilization, and storage consumption. To address the limitations in existing schemes, we propose an optimization based in-network caching policy, named as opt-Cache, which makes more efficient use of available cache resources, in order to reduce overall network utilization with reduced latency. Unlike existing schemes that mostly focus on a single factor to improve the cache performance, we intend to optimize the caching process by simultaneously considering various factors, e.g., content popularity, bandwidth, and latency, under a given set of constraints, e.g., available cache space, content availability, and careful eviction of existing contents in the cache. Our scheme determines optimized set of content to be cached at each node towards the edge based on content popularity and content distance from the content source. The contents that have less frequent requests have their popularity decreased with time. The optimal placement of contents across the CCN routers allows the overall reduction in bandwidth and latency. The proposed scheme is compared with the existing schemes and depicts better performance in terms of bandwidth consumption and latency while using less network resources.

Software Defined Networking (SDN) has been seen as a revolutionary and exciting network technology that aims to enable control and network management of various network types, whether wired or wireless. Nevertheless, SDN research focuses very little on wireless communication and, more specifically, on Wireless Mesh Networks (WMNs). Moreover, the issue of routing is vitally important in WMNs, but the legacy and traditional routing protocols cannot make the most of multiple paths between the source node and destination node due to the complexity and cost of the network. In this paper, we present SDNMesh, an SDN based routing architecture for WMNs. We combine SDN with WMN to allow mesh networks to meet current user requirements with several resources, coverage, and scalable high bandwidth capability. Apart from the mentioned capability, SDN’s unified approach leads to better network capacity management. Experiments have been carried out using the Mininet-WiFi simulation tool to create a network environment that allows integration of the two networking paradigms, centralized, and decentralized. Simulation results show that our SDNMesh routing solution performs better in terms of network performance metric throughput, packet loss ratio, and delay while comparing with traditional routing approaches such as OLSR, BATMAN, and an SDN based Three-Stage routing protocols. Moreover, experimental results show that SDNMesh gives better results in terms of the mentioned performance metrics.