Abstract: Apparatus and method of the present disclosure relates to facilitating at least one of wireless data communication and energy transfer using distributed beamforming in a wireless network. The present disclosure introduce a DBF scheme optimizes the receiver-end processing by significantly reducing the search-space for optimum phase shifts for each of the transmitters (102a . . . 102n). This contributes to significant reduction in energy expenditure at the energy-constrained receiver node. Also, a short broadcast feedback is employed to achieve high beamforming gain for several consecutive slots in the coherence time, thereby achieving high data rate information transfer and quick charging.

Abstract: Disclosed is system (100) and method (200) for providing energy management in communication network (700). The system (100) comprises a plurality of the solar-enabled Base Stations (BSs) (300-n) connected to a power grid (600) and a core network (800) configured in the communication network (700). The core network (800) comprises a controller (102) configured for enabling energy exchange among the energy-deficient BS from the plurality of BSs (300-n) and energy-sufficient BS from the plurality of BSs (300) through the power grid (600), thus reducing the carbon footprint. The controller (102) is also configured for cooperatively adjusting network coverage area for each of the BS of the plurality of BSs (300-n). The power grid (600) operates in an energy prosumer mode, providing flexibility to the plurality of BSs (300-n) to procure energy from it to avoid energy outage or sell surplus energy back to the grid.

Abstract: Disclosed are system (100) and method (300) for optimizing data transmission in communication network. System comprises Internet of Thing (IoT) device node (102) having sensors for capturing sensor data and controller (106) configured at the IoT device node for constructing set of attributes from the sensor data, such that each set of attribute comprises sensor data based on correlation. Support Vector Regression (SVR) models for pruning the sensor data at IoT device node (102) is defined. Values for each attribute in set of attributes predicted based on predicting run-time errors in values according to the SVR models defined for each of base attribute and the non-base attribute. The run-time errors for each of base attributes and non-base attributes gets compared with threshold error values. The IoT device node (102) transmits either model parameters for attributes or training data along with the set of attributes by transceiver (108) to the data collector node (104).

Abstract: A pollution monitoring system and a method is disclosed. The pollution monitoring system comprises an on-board sensor unit having a plurality of sensors, configured for monitoring air quality by measuring pollution data in the air and a power control unit connected with the on-board sensor unit for controlling the operation of the on-board sensor unit. The pollution monitoring system further comprises microcontroller configured for generating control signals to be transmitted to the power control unit for controlling operation of on-board sensor unit. The microcontroller is configured for receiving information from a base station regarding operation of the on-board sensor unit. Further, the pollution monitoring system comprises an air purification unit configured for receiving the pollution data from the microcontroller and enabling activation or deactivation of the air purification unit based on comparison of the pollution data with predefined threshold values.

Abstract: A smart sensing architecture (100) includes smart meters (102) and processing units (104). The smart meters (102) generate and transmit multidimensional data streams to the processing units (104). A processing unit (104) determines an optimum batch size for a multidimensional data stream and generates a multidimensional batch of data. The processing unit (104) reduces dimensionality of the multidimensional batch of data using principal component analysis to generate a low-dimensional batch of data and performs compressive sampling on the low-dimensional batch of data to generate a compressed batch of data, thereby saving bandwidth of transmission.

Abstract: A generalized multi-parameter mapping function aggregates decision criteria into a single virtual criterion to rank the potential relay candidates. Optimal rules for next hop relay as applicable to both transmitter-side selection and receiver-side election based forwarding schemes are also provided. Examples of network performance based on two optimization criteria include one-hop progress (greediness) and packet success rate (link quality). A suitable mapping function trades off the greediness for link quality. Simulation information is provided that indicates that the implementation according to the mapping function outperforms the reported transmitter-side link-aware forwarding schemes.

Abstract: A generalized multi-parameter mapping function aggregates decision criteria into a single virtual criterion to rank the potential relay candidates. Optimal rules for next hop relay as applicable to both transmitter-side selection and receiver-side election based forwarding schemes are also provided. Examples of network performance based on two optimization criteria include one-hop progress (greediness) and packet success rate (link quality). A suitable mapping function trades off the greediness for link quality. Simulation information is provided that indicates that the implementation according to the mapping function outperforms the reported transmitter-side link-aware forwarding schemes.