In this chapter, we have investigated how to exploit user resources in wireless networks.
Specifically, we have studied the problem of context-aware worker selection for max-imizing the worker performance in an MCS application with non-spatial tasks under missing knowledge about each worker’s individual performance. We have proposed a model for context-aware worker selection in MCS applications, which allows different task types to occur and which allows worker performance to be a possibly non-linear function of the task context and of the worker context. Then, taking a machine-learning-based approach, we have modeled the problem as a contextual MAB problem.
Moreover, we have proposed a context-aware hierarchical online learning algorithm for worker selection in MCS applications based on a hierarchical architecture of decision making. In the proposed algorithm, decision making and information collection is split among different entities. On the one hand, LCs located in each of the workers’ mobile devices learn their workers’ performances online over time, by regularly observing the workers’ personal contexts and their instantaneous performances. On the other hand, the centralized MCSP selects workers for tasks based on a regular information exchange with the LCs. This hierarchical coordination approach ensures that the most suitable workers are requested by the MCSP over time. Moreover, the learning in LCs ensures that personal worker context can be kept locally and does not need to be shared with the MCSP, but still workers are offered those tasks they are interested in the most.
The computational complexity of the algorithm has been shown to grow linearly with the dimension of the context space for the LCs and log-linearly with respect to the number of workers for the MCSP, respectively. Upper bounds on the local memory requirements of the proposed algorithm in the mobile devices as well as on the number of times the quality of each worker must be assessed have been derived. In addition, it has been shown that the more context dimensions the LCs are allowed to access, the lower are the communication requirements of the proposed hierarchical approach compared to an equivalent centralized approach. Besides, we have derived a sublinear upper bound on the regret, which analytically bounds the loss of the proposed algo-rithm with respect to an oracle that selects workers optimally under a priori knowledge about expected worker performance. The regret bound characterizes the learning speed and proves that the algorithm converges to the optimal worker selection strategy. Fi-nally, simulations based on synthetic and real data have shown that, depending on the availability of workers, the proposed algorithm achieves an up to 49% higher cu-mulative worker performance than the best algorithm from the literature by smartly exploiting context information for worker selection.
Chapter 6 Conclusions
6.1 Summary
The contributions of this thesis can be summarized as follows. In this thesis, we understand wireless networks asnetworks of distributed connected resources – a recent paradigm shift that is mandatory in view of the expected increases in the amount of data traffic, the number of wirelessly connected devices and the requirements of emerging mobile and IoT applications, all of which will pose heavy burdens on future wireless networks. Following this new paradigm, new techniques are needed that jointly consider and leverage different types of resources available in wireless networks, namely, communication, computation, caching, data collection and user resources, in order to improve the system performance. In this thesis, it is shown that such new techniques requirecontext-aware decision making in order to best exploit and allocate the different available resources. An overview of context-aware decision making is provided, by discussing context awareness, different types of architectures of decision making and different designs of decision agents. Finally, three candidate techniques for wireless networks are studied that jointly consider and leverage different types of resources, namely, computation offloading in multi-hop wireless networks, caching at the edge of wireless networks and MCS. For each technique, we identify a fundamental problem requiring aware decision making and we propose a novel framework for context-aware decision making that we use to solve the problem.
In Chapter 1, the need for the new paradigm of understanding wireless networks as networks of distributed connected resources is motivated. Moreover, the concept of context-aware decision making is introduced. Finally, three exemplary techniques are highlighted that jointly consider and leverage different types of resources of wireless networks. For each of the three techniques, a fundamental problem is identified and it is shown that context-aware decision making is required in order to best exploit the resources.
In Chapter 2, an overview of context-aware decision making in wireless networks is given. It is briefly outlined of which components a context-aware system model con-sists and the concept of context is introduced. Moreover, centralized, decentralized and hierarchical architectures of decision making are introduced and it is discussed for
which types of problems each of the architectures is suitable. Finally, different designs of decision agents and corresponding decision-making methods are discussed, with an emphasis on optimization and MAB frameworks, two specific types of approaches ap-pearing in this thesis.
In Chapter 3, it is studied how to trade computation resources off against communica-tion resources in wireless networks by considering computation offloading in multi-hop wireless networks. Using computation offloading, wirelessly connected devices may off-load computation tasks to resource-rich servers for remote computation and thereby reduce their task completion times and their energy consumption. The effect of compu-tation offloading on the energy consumption of an individual device depends not only on channel conditions and computing capabilities of the device, but also on task char-acteristics. Therefore, context information needs to be taken into account for deciding whether or not to offload a task. In this thesis, for the first time, we consider compu-tation offloading in multi-hop networks, where network coverage may be extended and required transmission power reduced. Since communication resources of relay nodes need to be used and shared for task offloading, offloading decisions are non-trivially coupled in multi-hop networks. Therefore, in this chapter, the fundamental problem of context-aware computation offloading for energy minimization in multi-hop wireless networks is identified. First, a novel model for context-aware computation offloading in multi-hop wireless networks is proposed that takes into account channel conditions, computing capabilities of the devices, task characteristics, and battery constraints at relay nodes. Based on this model, using an optimization-based approach, the prob-lem is formulated as a multi-dimensional knapsack probprob-lem, which takes into account the non-trivial coupling of offloading decisions. Then, using a centralized architecture of decision making, a new context-aware greedy heuristic algorithm for computation offloading in multi-hop networks is proposed. This algorithm enables a controller in the access point to take offloading decisions based on centrally collected information about network conditions and task context. The computational complexity of the pro-posed algorithm is analyzed and it is shown that the communication overhead of the proposed centralized architecture of decision making is small. Furthermore, numeri-cal results demonstrate that the proposed algorithm on average reduces the network energy consumption by 13% compared to the case when no computation offloading is used. Moreover, the proposed algorithm yields near-optimal results in the consid-ered offloading scenarios, with a maximal deviation of less than 6% from the global optimum.
In Chapter 4, it is investigated how to exploit caching resources in order to save com-munication resources in wireless networks by studying caching at the edge. Caching at the edge uses caching resources close to the mobile users to cache popular content in
a placement phase in order to locally serve user requests for this content in a delivery phase. In this way, the backhaul and cellular traffic may be alleviated and the latency for the user may be reduced. A crucial question is which content should be locally cached such that the number of cache hits is maximized. Caching the most popular content requires knowledge about the content popularity distribution, which is typi-cally not available a priori. Moreover, local content popularity may vary according to the preferences of the mobile users connecting to the local cache over time. The users’
preferences, in turn, may depend on their contexts. Finally, cache content placement needs to take into account the cache operator’s specific objective, which may include the need for service differentiation. Hence, in this chapter, the fundamental problem of context-aware proactive caching for cache hit maximization at the edge of the wireless network under missing knowledge about content popularity is identified. First, a new model for context-aware proactive caching is introduced, allowing different content to be favored by different users and including that the content popularity depends on the user’s context. Then, a machine-learning-based approach is pursued and the prob-lem is modeled as a contextual MAB probprob-lem. Based on this model, a novel online learning algorithm for context-aware proactive caching is proposed using a decentral-ized architecture of decision making. This algorithm enables the controller of a local cache to learn context-specific content popularity online over time and to take service differentiation into account. The computational complexity and the memory and com-munication requirements of the proposed algorithm are analyzed and it is shown how the algorithm can be extended to practical requirements. Furthermore, a sublinear upper bound on the regret of the algorithm is derived, which characterizes the learning speed and proves that the proposed algorithm converges to the optimal cache content placement strategy. Finally, simulations based on real data show that, depending on the cache size, the proposed algorithm achieves up to 27% more cache hits than the best algorithm taken from the literature.
In Chapter 5, it is studied how to make use of user resources in wireless networks by considering MCS. Using MCS, task owners outsource their tasks via an intermedi-ary MCSP to a set of workers, which allows different stakeholders to leverage human intelligence for task solving. Since different workers may have different interests and capabilities, not all of them may perform equally well on a given task. Hence, in order to maximize the worker performance on a given task under the task budget, the most suitable workers should be assigned to the task. Assigning the best workers to each task requires knowledge about the expected performance of each worker, which is typically not available a priori. Additionally, a worker’s performance may depend not only on the specific task, but also on the worker’s current context, and this dependency may be of non-linear nature. Furthermore, due to communication overhead and privacy
con-cerns of workers, it may be required to keep personal worker context locally instead of sharing it with the central MCSP, which makes it difficult for the MCSP to select the most suitable workers. Therefore, in this chapter, the fundamental problem of context-aware worker selection for maximizing the worker performance in an MCS application with non-spatial tasks under missing knowledge about each worker’s individual perfor-mance is identified. First, a novel model for context-aware worker selection in MCS is proposed that allows different task types to occur and that allows worker performance to be a possibly non-linear function of the task context and of the worker context.
Based on this model, a machine-learning-based approach is taken and the problem is modeled as a contextual MAB problem. Using a hierarchical architecture of decision making, a new context-aware hierarchical online learning algorithm for worker selec-tion in MCSis proposed. In the proposed algorithm, decision making and information collection is split among different entities. While a set of LCs located in the workers’
mobile devices learns the workers’ context-specific performances online over time, the centralized MCSP assigns workers to tasks based on a regular information exchange with the LCs. This novel hierarchical coordination approach ensures that the most suitable workers are requested to complete the task by the MCSP over time, while personal worker context is kept locally in the LCs, thus preserving the workers’ privacy and reducing communication overhead. The computational complexity of the proposed algorithm both for the LCs and the MCSP is analyzed. In addition, upper bounds on the local memory requirements of the proposed algorithm in the mobile devices as well as on the number of times the quality of each worker must be assessed are derived.
Moreover, it is shown that the more access to worker context is granted to the LCs, the lower are the communication requirements of the proposed algorithm compared to an equivalent centralized approach. Furthermore, a sublinear upper bound on the regret is derived, which characterizes the learning speed and proves that the proposed algorithm converges to the optimal worker selection strategy. Finally, numerical results based on synthetic and real data show that, depending on the availability of workers, the proposed algorithm achieves an up to 49% higher cumulative worker performance than the best algorithm from the literature.