In recent years, the Smart City concept has become popular for its promise to improve the quality of life of urban citizens. The concept involves multiple disciplines, such as Smart health care, Smart transportation, and Smart community. Most services in Smart Cities, especially in the Smart healthcare domain, require the real-time sharing, processing, and analyzing of Big Healthcare Data for intelligent decision making. Therefore, a strong wireless and mobile communication infrastructure is necessary to connect and access Smart healthcare services, people, and sensors seamlessly, anywhere at any time. In this scenario, mobile cloud computing (MCC) can play a vital role by offloading Big Healthcare Data related tasks, such as sharing, processing, and analysis, from mobile applications to cloud resources, ensuring quality of service demands of end users. Such resource migration, which is also termed virtual machine (VM) migration, is effective in the Smart healthcare scenario in Smart Cities. In this paper, we propose an ant colony optimization-based joint VM migration model for a heterogeneous, MCC-based Smart Healthcare system in Smart City environment. In this model, the user’s mobility and provisioned VM resources in the cloud address the VM migration problem. We also present a thorough performance evaluation to investigate the effectiveness of our proposed model compared with the state-of-the-art approaches.

In a Mobile Crowdsourcing System (MCS), a platform outsources sensing tasks to numerous mobile worker devices. The collected data are analyzed and the processed information is shared among many other interested users. The platform pays the workers for the sensing data and earns money from the users receiving processed information services. Distributing the sensing workloads among the potential workers so as to maintain the required data quality and to make a reasonable amount of profit is a challenging problem for such a platform. In this work, we develop a workload allocation policy that makes a reasonable trade-off between worker utilities and platform profit. It quantifies the utility (i.e., the quality of sensed data) of a worker as a function of worker mobility, current location and past sensing records. The workload allocation problem is formulated as a multi-objective non-linear programming (MONLP) problem which aims to make the desired trade-off between worker utilities and platform profit.The allocation problem is shown to be NP-hard and thus we develop two greedy algorithms with relaxed constraints to achieve polynomial time solutions. Performance of the proposed workload allocation policy is evaluated in a distributed computation environment using MATLAB. The results show its effectiveness compared to state-of-the-art methods in terms of platform profit, quality of sensing data and request service satisfaction.

Emergence of intelligent devices and mobile edge clouds (MECs) in 5G networks has exponentially increased the number of applications that demand low latency services. However, their resource heterogeneity, limited computing power and storage including congestion in the ultra-dense 5G network, make the real-time services challenging. Existing works are limited either by addressing application delay requirements or computational load balancing. This article develops an efficient resource allocation framework for selecting optimal servers and routing paths in the 5G MEC network by jointly optimizing latency, computational, and network load variances. First, we formulate the above multi-objective problem as a mixed-integer non-linear programming problem. Further, we adopt a hyper-heuristic (AWSH) algorithm by leveraging the combined powers of A nt Colony, W hale, S ine-Cosine, and H enry Gas Solubility Optimization algorithms. The proposed AWSH algorithm works at the higher level, and it explores and exploits one of the three lower-level heuristics in each iteration to efficiently capture the dynamically varying environmental parameters and thereby address the resource allocation problem. Their collaborative effort helps to achieve a global optimum in allocating resources of 5G MEC network. Simulation results prove the superiority of the AWSH algorithm compared to state-of-the-art solutions in terms of service latency, successful offloading ratio, and load balancing.

A conventional power grid is criticized by its poor capability of power usage management, especially in handling dynamically varying power demands over time. The concept of smart grid has been introduced to mitigate this problem by satisfying not only real-time power demands, but also by restricting power usage within the capacity. Its consistent outperformance and new perspective in computer intelligence to control the grid for autonomous power consumption has been gradually replacing the conventional power grid. However, even in smart grid, providing high satisfaction to users often leads smart grid operator (SGO) to loss and vice versa. In this paper, we develop an optimal dynamic pricing mechanism for trading-off (ODPT), for SGOs that tradeoff between user utility and operator profit in smart grid systems. It allows the operator to purchase power from multiple energy producers and to set selling price to users dynamically following the demand-supply theory of economics. It also exploits an artificial neural network model to more accurately predict the power usage. The simulation results, carried out on a commercially available optimization modeling tool using practical power usage data, prove the effectiveness of the proposed ODPT in increasing the operator profit while satisfying user demands.