The substantial advancements offered by the edge computing has indicated serious evolutionary improvements for the internet of things (IoT) technology. The rigid design philosophy of the traditional network architecture limits its scope to meet future demands. However, information centric networking (ICN) is envisioned as a promising architecture to bridge the huge gaps and maintain IoT networks, mostly referred as ICN-IoT. The edge-enabled ICN-IoT architecture always demands efficient in-network caching techniques for supporting better user’s quality of experience (QoE). In this paper, we propose an enhanced ICN-IoT content caching strategy by enabling artificial intelligence (AI)-based collaborative filtering within the edge cloud to support heterogeneous IoT architecture. This collaborative filtering-based content caching strategy would intelligently cache content on edge nodes for traffic management at cloud databases. The evaluations has been conducted to check the performance of the proposed strategy over various benchmark strategies, such as LCE, LCD, CL4M, and ProbCache. The analytical results demonstrate the better performance of our proposed strategy with average gain of 15% for cache hit ratio, 12% reduction in content retrieval delay, and 28% reduced average hop count in comparison to best considered LCD. We believe that the proposed strategy will contribute an effective solution to the related studies in this domain.

Today, with the worldwide offer and rapid increment in multimedia applications on the web, the demands of users to get them accessed are also increasing prominently. The users in vehicular environment too expect efficient multimedia streaming while travelling on the road. However, the high mobility of vehicles as well as the limited transmission range of infrastructure components in IP based network provides low performance by offering high delay and additional network overhead. To provide better Quality of Experience (QoE) with high performance, Information Centric Networking (ICN) is blended with vehicular environment. Caching the content inside network nodes is inherent feature of ICN with various associated benefits such as low content retrieval delay, less network traffic, path reduction and so on. However, challenges still exists for caching the content due to resource constrained network environment (such as limited cache capacity, node battery) as well as for secure delivery of cached data. To solve these challenges and to enhance network performance, we propose a cooperative caching scheme in hierarchical network architecture that jointly considers cache location as well as combined content popularity and predicted future rating score while making caching decision. The proposed approach uses two layer hierarchical architecture where nodes in edge layer are divided into clusters. The proposed scheme uses modified Weighted Clustering Algorithms (WCA) for selection of cluster heads which are then used to decide cache location. A probability matrix is used to compute content caching probability which considers both popularity and predicted future rating of content. The proposed approach dynamically predict the user’s preferences using non-negative matrix factorization (NMF) - a machine learning technique which eventually provides prediction of future rating. Based on the selection of both cache location and content to cache, the proposed scheme can effectively cache the content in the network. Further, to deal with the secure delivery of cached content, this work supports legitimate user authorization at edge nodes. The performance of the proposed scheme is evaluated in MATLAB parallel computing toolkit. The results prove significant caching improvement in terms of cache hit, hop reduction and average delay using our proposed scheme.

Wireless Sensor Networks (WSNs) and the Internet of Things (IoT) have emerged as transforming technologies, bringing the potential to revolutionize a wide range of industries such as environmental monitoring, agriculture, manufacturing, smart health, home automation, wildlife monitoring, and surveillance. Population expansion, changes in the climate, and resource constraints all offer problems to modern IoT applications. To solve these issues, the integration of Wireless Sensor Networks (WSNs) and the Internet of Things (IoT) has come forth as a game-changing solution. For example, in agricultural environment, IoT-based WSN has been utilized to monitor yield conditions and automate agriculture precision through different sensors. These sensors are used in agriculture environments to boost productivity through intelligent agricultural decisions and to collect data on crop health, soil moisture, temperature monitoring, and irrigation. However, sensors have finite and non-rechargeable batteries, and memory capabilities, which might have a negative impact on network performance. When a network is distributed over a vast area, the performance of WSN-assisted IoT suffers. As a result, building a stable and energy-efficient routing infrastructure is quite challenging in order to extend network lifetime. To address energy-related issues in scalable WSN-IoT environments for future IoT applications, this research proposes EEDC: An Energy Efficient Data Communication scheme by utilizing “Region based Hierarchical Clustering for Efficient Routing (RHCER)”—a multi-tier clustering framework for energy-aware routing decisions. The sensors deployed for IoT application data collection acquire important data and select cluster heads based on a multi-criteria decision function. Further, to ensure efficient long-distance communication along with even load distribution across all network nodes, a subdivision technique was employed in each tier of the proposed framework. The proposed routing protocol aims to provide network load balancing and convert communicating over long distances into shortened multi-hop distance communications, hence enhancing network lifetime.The performance of EEDC is compared to that of some existing energy-efficient protocols for various parameters. The simulation results show that the suggested methodology reduces energy usage by almost 31% in sensor nodes and provides almost 38% improved packet drop ratio.

Since the worldwide Internet of Things (IoT) in smart cities is becoming increasingly popular among consumers and the business community, network traffic management is a crucial issue for optimizing the IoT ’s performance in smart cities. Multiple controllers on a immense scale implement in Software Defined Networks (SDN) in integration with Internet of Things (IoT) as an emerging paradigm enhances the scalability, security, privacy, and flexibility of the centralized control plane for smart city applications. The distributed multiple controller implementation model in SDN-IoT cannot conform to the dramatic developments in network traffic which results in a load disparity between controllers, leading to higher packet drop rate, high response time, and other problems with network performance deterioration. This paper lays the foundation on the multiple distributed controller load balancing (MDCLB) algorithm on an immense-scale SDN-IoT for smart cities. A smart city is a residential street that uses information and communication technology (ICT) and the Internet of Things (IoT) to improve its citizens’ quality of living. Researchers then propose the algorithm on the unbalancing of the load using the multiple controllers based on the parameter CPU Utilization in centralized control plane. The experimental results analysis is performed on the emulator named as mininet and validated the results in ryu controller over dynamic load balancing based on Nash bargaining, efficient switch migration load balancing algorithm, efficiency aware load balancing algorithm, and proposed algorithm (MDCLB) algorithm are executed and analyzed based on the parameter CPU Utilization which ensures that the Utilization of CPU with load balancing is 20% better than the Utilization of CPU without load balancing.

The Information Centric Networking (ICN) is a future internet architecture to support efficient content distribution in a vehicular environment. In-network caching in ICN provides a realistic solution for vehicular communication due to storage of content replicas inside network vehicles. However, the challenge still exists while caching content replicas in resource constraint vehicles ( such as limited power and cache capacity) to minimize the communication latency. To address the above mentioned challenge, this paper proposes EPC - an ICN based Energy efficient Placement of Content chunk that fits well in a vehicular environment. The proposed resource management strategy mainly aims to reduce the content fetching delay by caching content replicas towards the network edge router. The EPC strategy decides on placement of content chunks on each vehicle by jointly considering residual power of current vehicle, local popularity of content, and caching gain. The EPC supports efficient utilization of network available resources by allowing only vehicles with their residual power greater than threshold to perform chunk caching and hence, further offers reduced content duplication in the whole network. The effectiveness of the proposed scheme is evaluated in Icarus- an ICN simulator for analyzing the performance of ICN caching and routing strategies. The EPC outperforms various state of the art caching strategies approximately by 30% when gets evaluated in terms of offered cache hit ratio, content retrieval delay, and the average number of hops utilized for fetching the requested content.

The pervasive growth in the variety of consumer electronic devices (CEDs), as well as the Internet traffic, has evolved various security risks toward their usage. This article presents the concept of communications without a centralized server to facilitate the fastest communication between end-devices, e.g., user handheld devices, and edge devices. Three communication scenarios are presented to support authentication in the proposed architecture. To understand the concept, this article first describes the growth in the CEDs market. Afterward, the discussion on various challenges faced for incorporating security in such devices is presented along with edge-based security solutions. Finally, the article presents a use-case of the content delivery network to discuss various security solutions based on the edge with the hope that these solutions will work for future security.