In this article we provide a comprehensive review of the existing literature on techniques and protocols for in-network aggregation in wireless sensor networks. We first define suitable criteria to classify existing solutions, and then describe them by separately addressing the different layers of the protocol stack while highlighting the role of a cross-layer design approach, which is likely to be needed for optimal performance. Throughout the article we identify and discuss open issues, and propose directions for future research in the area

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In-network aggregation techniques for wireless sensor networks: a survey

All one needs to know about fog computing and related edge computing paradigms: A complete survey

Internet of Things (IoT) devices can apply mobile edge computing (MEC) and energy harvesting (EH) to provide high-level experiences for computational intensive applications and concurrently to prolong the lifetime of the battery. In this paper, we propose a reinforcement learning (RL) based offloading scheme for an IoT device with EH to select the edge device and the offloading rate according to the current battery level, the previous radio transmission rate to each edge device, and the predicted amount of the harvested energy. This scheme enables the IoT device to optimize the offloading policy without knowledge of the MEC model, the energy consumption model, and the computation latency model. Further, we present a deep RL-based offloading scheme to further accelerate the learning speed. Their performance bounds in terms of the energy consumption, computation latency, and utility are provided for three typical offloading scenarios and verified via simulations for an IoT device that uses wireless power transfer for energy harvesting. Simulation results show that the proposed RL-based offloading scheme reduces the energy consumption, computation latency, and task drop rate, and thus increases the utility of the IoT device in the dynamic MEC in comparison with the benchmark offloading schemes.

Learning-Based Computation Offloading for IoT Devices With Energy Harvesting

With the Internet of Things (IoT) becoming part of our daily life and our environment, we expect rapid growth in the number of connected devices. IoT is expected to connect billions of devices and humans to bring promising advantages for us. With this growth, fog computing, along with its related edge computing paradigms, such as multi-access edge computing (MEC) and cloudlet, are seen as promising solutions for handling the large volume of security-critical and time-sensitive data that is being produced by the IoT. In this paper, we first provide a tutorial on fog computing and its related computing paradigms, including their similarities and differences. Next, we provide a taxonomy of research topics in fog computing, and through a comprehensive survey, we summarize and categorize the efforts on fog computing and its related computing paradigms. Finally, we provide challenges and future directions for research in fog computing.

All One Needs to Know about Fog Computing and Related Edge Computing Paradigms: A Complete Survey

Research in the Internet of Things (IoT) conceives a world where everyday objects are connected to the Internet and exchange, store, process, and collect data from the surrounding environment. IoT devices are becoming essential for supporting the delivery of data to enable electronic services, but they are not sufficient in most cases to host application services directly due to their intrinsic resource constraints. Fog Computing (FC) can be a suitable paradigm to overcome these limitations, as it can coexist and cooperate with centralized Cloud systems and extends the latter toward the network edge. In this way, it is possible to distribute resources and services of computing, storage, and networking along the Cloud-to-Things continuum. As such, FC brings all the benefits of Cloud Computing (CC) closer to end (user) devices. This article presents a survey on the employment of FC to support IoT devices and services. The principles and literature characterizing FC are described, highlighting six IoT application domains that may benefit from the use of this paradigm. The extension of Cloud systems towards the network edge also creates new challenges and can have an impact on existing approaches employed in Cloud-based deployments. Research directions being adopted by the community are highlighted, with an indication of which of these are likely to have the greatest impact. An overview of existing FC software and hardware platforms for the IoT is also provided, along with the standardisation efforts in this area initiated by the OpenFog Consortium (OFC).

Fog Computing for the Internet of Things: A Survey

Edge cloud computing moves cloud services to the edge of the network, thereby allowing clients to access services with a significantly reduced network delay. This service migration is intended to enable a range of latency sensitive mobile applications. In this paper, we propose to manage user QoS by actively migrating services to different edge clouds in response to degraded server or network performance. Previous studies have proposed a distance-based Markov Decision Process (MDP) for optimizing migration decisions. These models provide the feasibility of applying MDP to edge cloud service migration decisions. However, these models fail to consider dynamic network and server states in migration decisions. In this work, we address these limitations by designing a comprehensive edge cloud migration decision system, which we call SEGUE. SEGUE achieves optimal migration decisions by providing a long-term optimal QoS to mobile users in the presence of link quality and server load variation. The basis of SEGUE is in its QoS-aware service migration and its state based MDP model which effectively incorporates the two dominant factors in making migration decisions: 1) network state, and 2) server state. An evaluation of SEGUE performance is given through an augmented reality application. Our results demonstrate that SEGUE reduces the response time of this application by 27.21% and 53.70% compared to the lowest load migration model and the least hop migration model, respectively.

SEGUE: Quality of Service Aware Edge Cloud Service Migration

In this paper we present an approach to parallel programming called SpiceC. SpiceC simplifies the task of parallel programming through a combination of an intuitive computation model and SpiceC directives. The SpiceC parallel computation model consists of multiple threads where every thread has a private space for data and all threads share data via a shared space. Each thread performs computations using its private space thus offering isolation which allows for speculative computations. SpiceC provides easy to use SpiceC compiler directives using which the programmers can express different forms of parallelism. It allows developers to express high level constraints on data transfers between spaces while the tedious task of generating the code for the data transfers is performed by the compiler. SpiceC also supports data transfers involving dynamic data structures without help from developers. SpiceC allows developers to create clusters of data to enable parallel data transfers. SpiceC programs are portable across modern chip multiprocessor based machines that may or may not support cache coherence. We have developed implementations of SpiceC for shared memory systems with and without cache coherence. We evaluate our implementation using seven benchmarks of which four are parallelized speculatively. Our compiler generated implementations achieve speedups ranging from 2x to 18x on a 24 core system.

SpiceC: scalable parallelism via implicit copying and explicit commit

This paper studies the energy efficient routing for data aggregation in wireless sensor networks. The data aggregation tree is a tree where the root of the tree is the data center called the sink node and the other nodes are sensor nodes. The sensor nodes sense the data and pass the data back to the data center along the data aggregation tree. We consider a real-time scenario where the data aggregation must be performed within a specified latency constraint. The objective is to minimize the overall energy cost of the sensor nodes for data aggregation subject to the latency constraint. The original contributions of the paper include: 1) Development of an analytic model for IEEE Standard 802.15.4 CSMA-CA to compute the worst case delay for a sensor node to aggregate the data from all its child nodes in the aggregation tree; 2) Proposal of a heuristic algorithm for constructing data aggregation trees that minimize total energy cost under the latency bound obtained from our analytical model. Extensive simulations have been conducted and the results verify the validity of the proposed analytical model and the superior performance of the proposed algorithm for constructing aggregation trees.

Energy efficient real-time data aggregation in wireless sensor networks

A fundamental challenge of managing mutable data replication in a Peer-to-Peer (P2P) system is how to efficiently maintain consistency under various sharing patterns with heterogeneous resource capabilities. This paper presents a framework for balanced consistency maintenance (BCoM) in structured P2P systems. Replica nodes of each object are organized into a tree for disseminating updates, and a sliding window update protocol is developed to bound the consistency. The effect of window size in response to dynamic network conditions, workload updates and resource limits is analyzed through a queueing model. This enables us to balance availability, performance and consistency strictness for various application requirements. On top of the dissemination tree, two enhancements are proposed: a fast recovery scheme to strengthen the robustness against node and link failures; and a node migration policy to remove and prevent the bottleneck for better system performance. Simulations are conducted using P2PSim to evaluate BCoM in comparison to SCOPE \cite{SCOPE}. The experimental results demonstrate that BCoM significantly improves the availability of SCOPE by lowering the discard rate from almost $100\%$ to $5\%$ with slight increase in latency.

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A Balanced Consistency Maintenance Protocol for Structured P2P Systems

A fundamental challenge of supporting mutable data replication in a Peer-to-Peer (P2P) system is to efficiently maintain consistency. This paper presents a framework for Balanced Consistency Maintenance (BCoM) in structured P2P systems with heterogeneous node capabilities and various workload patterns. Replica nodes of each object are organized into a tree structure for disseminating updates, and a sliding window update protocol is developed for consistency maintenance. We present an analytical model to optimize the window size according to the dynamic network conditions, workload patterns and resource limits. In this way, BCoM balances the consistency strictness, object availability for updates, and update propagation performance for various application requirements. On top of the dissemination tree, two enhancements are proposed: (1) a fast recovery scheme to strengthen the robustness against node and link failures, and (2) a node migration policy to remove and prevent bottlenecks allowing more efficient update delivery. Simulations are conducted using P2PSim to evaluate BCoM in comparison to SCOPE [1]. The experimental results demonstrate that BCoM outperforms SCOPE with lower discard rates. BCoM achieves a discard rate as low as 5 percent in most cases while SCOPE has almost 100 percent discard rate.

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Yi Hu

Papers

SEGUE: Quality of Service Aware Edge Cloud Service Migration

SpiceC: scalable parallelism via implicit copying and explicit commit

Energy efficient real-time data aggregation in wireless sensor networks

A Balanced Consistency Maintenance Protocol for Structured P2P Systems

Maintaining Data Consistency in Structured P2P Systems