Wireless networks have revolutionized nowadays world by providing real time cost-efficient service and connectivity. Even such an unprecedented level of service could not fulfill the insatiable desire of the modern world for more advanced technologies. As a result, a great deal of attention has been directed towards (mobile) wireless sensor networks (WSNs) which are comprised of considerably cheap nodes that can cooperate to perform complex tasks in a distributed fashion in extremely harsh environments. Unique features of wireless environments, added complexity owing to mobility, distributed nature of the network setup, and tight performance and energy constraints, pose a challenge for researchers to devise systems which strike a proper balance between performance and resource utilization. We study some of the fundamental challenges of wireless (sensor) networks associated with resource efficiency, scalability, and location awareness. The pivotal point which distinguishes our studies from existing literature is employing the concept of sparse reconstruction and compressive sensing (CS) in our problem formulation and system design. We explore sparse structures embedded within the models we deal with and try to benefit from the undersampling offered by incorporating sparsity and thereby developing sparsity-aware system-level solutions. We prove that looking at these challenges from our perspective not only guarantees an expected cost efficiency due to taking less measurements, but also if properly designed, can promise an acceptable accuracy.