Autonomic Computing is a concept that brings together many fields of computing with the purpose of creating computing systems that self-manage. In its early days it was criticised as being a "hype topic" or a rebadging of some Multi Agent Systems work. In this survey, we hope to show that this was not indeed 'hype' and that, though it draws on much work already carried out by the Computer Science and Control communities, its innovation is strong and lies in its robust application to the specific self-management of computing systems. To this end, we first provide an introduction to the motivation and concepts of autonomic computing and describe some research that has been seen as seminal in influencing a large proportion of early work. Taking the components of an established reference model in turn, we discuss the works that have provided significant contributions to that area. We then look at larger scaled systems that compose autonomic systems illustrating the hierarchical nature of their architectures. Autonomicity is not a well defined subject and as such different systems adhere to different degrees of Autonomicity, therefore we cross-slice the body of work in terms of these degrees. From this we list the key applications of autonomic computing and discuss the research work that is missing and what we believe the community should be considering. ACM Reference Format:Huebscher, M. C. and McCann, J. A. 2008. A survey of autonomic computing-degrees, models, and applications.

Technologies to support the Internet of Things are becoming more important as the need to better understand our environments and make them smart increases. As a result it is predicted that intelligent devices and networks, such as WSNs, will not be isolated, but connected and integrated, composing computer networks. So far, the IP-based Internet is the largest network in the world; therefore, there are great strides to connect WSNs with the Internet. To this end, the IETF has developed a suite of protocols and open standards for accessing applications and services for wireless resource constrained networks. However, many open challenges remain, mostly due to the complex deployment characteristics of such systems and the stringent requirements imposed by various services wishing to make use of such complex systems. Thus, it becomes critically important to study how the current approaches to standardization in this area can be improved, and at the same time better understand the opportunities for the research community to contribute to the IoT field. To this end, this article presents an overview of current standards and research activities in both industry and academia

Fifth generation (5G) wireless networks face various challenges in order to support large-scale heterogeneous traffic and users, therefore new modulation and multiple access (MA) schemes are being developed to meet the changing demands. As this research space is ever increasing, it becomes more important to analyze the various approaches, therefore in this article we present a comprehensive overview of the most promising modulation and MA schemes for 5G networks. We first introduce the different types of modulation that indicate their potential for orthogonal multiple access (OMA) schemes and compare their performance in terms of spectral efficiency, out-of-band leakage, and bit-error rate. We then pay close attention to various types of non-orthogonal multiple access (NOMA) candidates, including power-domain NOMA, code-domain NOMA, and NOMA multiplexing in multiple domains. From this exploration we can identify the opportunities and challenges that will have significant impact on the design of modulation and MA for 5G networks.

Abstract-The increasing pervasiveness of Wireless Sensor Networks (WSNs) in diverse application domains including critical infrastructure systems, sets an extremely high security bar in the design of WSN systems to exploit their full benefits, increasing trust while avoiding loss. Nevertheless, a combination of resource restrictions and the physical exposure of sensor devices inevitably cause such networks to be vulnerable to security threats, both external and internal. While several researchers have provided a set of open problems and challenges in WSN security and privacy, there is a gap in the systematic study of the security implications arising from the nature of existing communication protocols in WSNs. Therefore, we have carried out a deep-dive into the main security mechanisms and their effects on the most popular protocols and standards used in WSN deployments i.e. IEEE 802.15.4, B-MAC, 6LoWPAN, RPL, BCP, CTP, and CoAP, where potential security threats and existing countermeasures are discussed at each layer of WSN stack. This work culminates in a deeper analysis of network layer attacks deployed against the RPL routing protocol. We quantify the impact of individual attacks on the performance of a network using the Cooja network simulator. Finally, we discuss new research opportunities in network layer security and how to use Cooja as a benchmark for developing new defenses for WSN systems.

Abstract-The growth of Internet of Things (IoT) devices with multiple radio interfaces has resulted in a number of urbanscale deployments of IoT multinetworks, where heterogeneous wireless communication solutions coexist (e.g. WiFi, Bluetooth, Cellular). Managing the multinetworks for seamless IoT access and handover, especially in mobile environments, is a key challenge. Software-defined networking (SDN) is emerging as a promising paradigm for quick and easy configuration of network devices, but its application in urban-scale multinetworks requiring heterogeneous and frequent IoT access is not well studied. In this paper we present UbiFlow, the first softwaredefined IoT system for combined ubiquitous flow control and mobility management in urban heterogeneous networks. UbiFlow adopts multiple controllers to divide urban-scale SDN into different geographic partitions and achieve distributed control of IoT flows. A distributed hashing based overlay structure is proposed to maintain network scalability and consistency. Based on this UbiFlow overlay structure, the relevant issues pertaining to mobility management such as scalable control, fault tolerance, and load balancing have been carefully examined and studied. The UbiFlow controller differentiates flow scheduling based on per-device requirements and whole-partition capabilities. Therefore, it can present a network status view and optimized selection of access points in multinetworks to satisfy IoT flow requests, while guaranteeing network performance for each partition. Simulation and realistic testbed experiments confirm that UbiFlow can successfully achieve scalable mobility management and robust flow scheduling in IoT multinetworks; e.g. 67.21% throughput improvement, 72.99% reduced delay, and 69.59% jitter improvements, compared with alternative SDN systems.

The assessment of node-to-node similarities based on graph topology arises in a myriad of applications, e.g., web search. SimRank is a notable measure of this type, with the intuition that "two nodes are similar if their in-neighbors are similar". While most existing work retrieving SimRank only considers all-pairs SimRank s(⋆, ⋆) and single-source SimRank s(⋆, j) (scores between every node and query j), there are appealing applications for partial-pairs SimRank, e.g., similarity join. Given two node subsets A and B in a graph, partial-pairs SimRank assessment aims to retrieve only {s(a, b)} ∀a∈A,∀b∈B . However, the best-known solution appears not self-contained since it hinges on the premise that the SimRank scores with node-pairs in an h-go cover set must be given beforehand. This paper focuses on efficient assessment of partial-pairs SimRank in a self-contained manner. (1) We devise a novel "seed germination" model that computes partial-pairs SimRank in O(k|E| min{|A|, |B|}) time and O(|E|+k|V |) memory for k iterations on a graph of |V | nodes and |E| edges.(2) We further eliminate unnecessary edge access to improve the time of partial-pairs SimRank to O(m min{|A|, |B|}), where m ≤ min{k|E|, ∆ 2k }, and ∆ is the maximum degree. (3) We show that our partial-pairs SimRank model also can handle the computations of all-pairs and single-source SimRanks. (4) We empirically verify that our algorithms are (a) 38x faster than the best-known competitors, and (b) memory-efficient, allowing scores to be assessed accurately on graphs with tens of millions of links.

Abstract-Reliable real-time sensing plays a vital role in ensuring the reliability and safety of industrial Cyber-Physical Systems (CPSs) such as wireless sensor and actuator networks. For many reasons, such as harsh industrial environments, faultprone sensors, or malicious attacks, sensor readings may be abnormal or faulty. This could lead to serious system performance degradation or even catastrophic failure. Current anomaly detection approaches are either centralized and complicated, or restricted due to strict assumptions, which are not suitable for practical large-scale Networked Industrial Sensing Systems (NISSs) where sensing devices are connected via digital communications, such as wireless sensor networks or smart grid systems. In this paper, we introduce a fully distributed general-anomalydetection (GAD) scheme, which uses graph theory and exploits spatiotemporal correlations of physical processes to carry out real-time anomaly detection for general large-scale NISSs. We formally prove the scalability of our GAD approach and evaluate the performance of GAD for two industrial applications: building structure monitoring and smart grids. Extensive trace-driven simulations validate our theoretical analysis, and demonstrate that our approach can significantly outperform state-of-the-art approaches in terms of detection accuracy and efficiency.

The emergence of the water–energy–food (WEF) nexus has resulted in changes to the way we perceive our natural resources. Stressors such as climate change and population growth have highlighted the fragility of our WEF systems, necessitating integrated solutions across multiple scales. While a number of frameworks and analytical tools have been developed since 2011, a comprehensive WEF nexus tool remains elusive, hindered in part by our limited data and understanding of the interdependencies and connections across the WEF systems. To achieve this, the community of academics, practitioners and policy‐makers invested in WEF nexus research are addressing several critical areas that currently remain as barriers. First, the plurality of scales (e.g., spatial, temporal, institutional, jurisdictional) necessitates a more comprehensive effort to assess interdependencies between water, energy and food, from household to institutional and national levels. Second, and closely related to scale, a lack of available data often hinders our ability to quantify physical stocks and flows of resources. Overcoming these barriers necessitates engaging multiple stakeholders, and using experiences and local insights to better understand nexus dynamics in particular locations or scenarios, and we exemplify this with the inclusion of a UK‐based case study on exploring the nexus in a particular geographical area. We elucidate many challenges that have arisen across nexus research, including the impact of multiple scales in operation, and concomitantly, what impact these scales have on data accessibility. We assess some of the critical frameworks and tools that are applied by nexus researchers and articulate some of the steps required to develop from nexus thinking to an operationalisable concept, with a consistent focus on scale and data availability.