Lightweight Caching and Load Balancing for Efficient Content Delivery in Information-Centric IoT

<p>In recent years, Information-Centric Networking (ICN) has emerged as a promising candidate for a future Internet architecture. While originally designed with the traditional Internet in mind, it has also been identified as a potential replacement for current Internet of Things (IoT) networking solutions. However, applications in the IoT face a number of unique challenges due to the constrained nature of the hardware. One of these challenges is that available memory is often extremely limited. This thesis aims to evaluate the feasibility of using ICN in-network caching on IoT devices in order to achieve efficient content delivery. It evaluates the performance of existing approaches on constrained hardware and explores how the technology can be improved and tailored towards that environment. Existing strategies are found to be lacking in key aspects, particularly the fact that the effects of network topology are often not considered when making caching decisions. It is shown that approaches based on network centrality are promising, but existing implementations are not suited for constrained hardware. Therefore, a lightweight in-network caching strategy called Approximate Betweenness Centrality (ABC) is proposed that takes the specific requirements of IoT into consideration and allows for efficient cache placement regardless of network topology. Then, a modular solution for load balancing through off-path caching is presented to address potential shortcomings of the centrality-based caching approach. It allows the network to make more efficient use of available caching resources without introducing additional overhead. Furthermore, solutions for ensuring Quality of Service (QoS) are discussed. The expanded role of caching strategies under such QoS constraints is explored and their performance is evaluated. This thesis shows that it is possible to design and deploy lightweight, low-overhead solutions on constrained hardware. Using a realistic deployment of physical IoT devices, it is demonstrated that these approaches can reach satisfactory levels of performance.</p>

Lightweight Caching and Load Balancing for Efficient Content Delivery in Information-Centric IoT

<p>In recent years, Information-Centric Networking (ICN) has emerged as a promising candidate for a future Internet architecture. While originally designed with the traditional Internet in mind, it has also been identified as a potential replacement for current Internet of Things (IoT) networking solutions. However, applications in the IoT face a number of unique challenges due to the constrained nature of the hardware. One of these challenges is that available memory is often extremely limited. This thesis aims to evaluate the feasibility of using ICN in-network caching on IoT devices in order to achieve efficient content delivery. It evaluates the performance of existing approaches on constrained hardware and explores how the technology can be improved and tailored towards that environment. Existing strategies are found to be lacking in key aspects, particularly the fact that the effects of network topology are often not considered when making caching decisions. It is shown that approaches based on network centrality are promising, but existing implementations are not suited for constrained hardware. Therefore, a lightweight in-network caching strategy called Approximate Betweenness Centrality (ABC) is proposed that takes the specific requirements of IoT into consideration and allows for efficient cache placement regardless of network topology. Then, a modular solution for load balancing through off-path caching is presented to address potential shortcomings of the centrality-based caching approach. It allows the network to make more efficient use of available caching resources without introducing additional overhead. Furthermore, solutions for ensuring Quality of Service (QoS) are discussed. The expanded role of caching strategies under such QoS constraints is explored and their performance is evaluated. This thesis shows that it is possible to design and deploy lightweight, low-overhead solutions on constrained hardware. Using a realistic deployment of physical IoT devices, it is demonstrated that these approaches can reach satisfactory levels of performance.</p>

Information-Centric Networking Cache Placement Method Based on Cache Node Status and Location

Information-Centric Networking with caching is a very promising future network architecture. The research on its cache deployment strategy is divided into three categories, namely, noncooperative cache, explicit collaboration cache, and implicit collaboration cache. Noncooperative caching can cause problems such as high content repetition rate in the web cache space. Explicit collaboration caching generally reflects the best caching effect but requires a lot of communication to satisfy the exchange of cache node information and depends on the controller to perform the calculation. On this basis, implicit cooperative caching can reduce the information exchange and calculation between cache nodes while maintaining a good caching effect. Therefore, this paper proposes an on-path implicit cooperative cache deployment method based on the dynamic LRU-K cache replacement strategy. This method evaluates the cache nodes based on their network location and state and selects the node with the best state value on the transmission path for caching. Each request will only select one or two nodes for caching on the request path to reduce the redundancy of the data. Simulation experiments show that the cache deployment method based on the state and location of the cache node can improve the hit rate and reduce the average request length.

Joint Successful Transmission Probability, Delay, and Energy Efficiency Caching Optimization in Fog Radio Access Network

The fog radio access network (F-RAN) is considered an efficient architecture for caching technology as it can support both edge and centralized caching due to the backhauling of the fog access points (F-APs). Successful transmission probability (STP), delay, and energy efficiency (EE) are key performance metrics for F-RAN. Therefore, this paper proposes a proactive cache placement scheme that jointly optimizes STP, delay, and EE in wireless backhauled cache-enabled F-RAN. First, expressions of the association probability, STP, average delay, and EE are derived using stochastic geometry tools. Then, the optimization problem is formulated to obtain the optimal cache placement that maximizes the weighted sum of STP, EE, and negative delay. To solve the optimization problem, this paper proposes the normalized cuckoo search algorithm (NCSA), which is a novel modified version of the cuckoo search algorithm (CSA). In NCSA, after generating the solutions randomly via Lévy flight and random walk, a simple bound is applied, and then the solutions are normalized to assure their feasibility. The numerical results show that the proposed joint cache placement scheme can effectively achieve significant performance improvement by up to 15% higher STP, 45% lower delay, and 350% higher EE over the well-known benchmark caching schemes.